MINISTRY FOR EDUCATION AND SCIENCE, RUSSIAN FEDERATION
FEDERAL STATW AUTONOMOUS ORGANIZATION OF HIGHER EDUCATION
«NOVOSIBIRSK NATIONAL RESEARCH STATE UNIVERSITY»
(NOVOSIBRSK STATE UNIVERSITY, NSU)
Faculty
Economics
_____
Chair
Political Economy
_____________________________
Department
Management ____________________________________
Master Educational program
Oil and gas Management
_________________
GRADUATE QUALIFICATION PAPAER
MASTER'S DISSERTATION
Shadrin Sergey
Paper title
Ways of increase in capitalization of oil and gas companies
«Admitted to defense»
The head of the chair:
Professor
Scientific Supervisor,
D-r of Econ. Sciences,
D-r of Econ. Sciences, Professor
Eder L.V./………...
Filimonova I.V./…………..
«……»………………20…г.
«……»………………20…г..
Date of defense: «……»………………20…г.
Novosibirsk, 2017
2
Table of contest
Introduction ................................................................................................................................... 3
Chapter 1. Capitalization and its role in development of the oil and gas companies ............. 6
1.1.
Concept and types of capitalization.................................................................................... 6
1.2.
The main indicators of economic development of the countries and factors influencing on the
capitalization............................................................................................................................... 12
1.3.
Key characteristics in influencing on the capitalization of a state and prospect of
development of the world oil and gas market .............................................................................. 14
Chapter 2. Methodological bases of big data and machine learning ...................................... 19
2.1.
2.2.
2.3.
Big Data. What it is and why it matters?........................................................................... 19
Machine Learning. What it is and why it matters? ............................................................ 22
Description of the companies of oil and gas companies .................................................... 28
Chapter 3. Development of a method of machine learning for increase the capitalization of
the oil and gas companies of Russia. .......................................................................................... 39
3.1.
Sequence of a research. ................................................................................................... 39
3.2.
Application of machine learning for the analysis of dependence of capitalization on various
factors of activity of the oil and gas companies ............................................................................ 42
3.3.
Results and recommendations. ........................................................................................ 47
Conclusion .................................................................................................................................... 49
References .................................................................................................................................... 51
Appendix .......................................................................................... Error! Bookmark not defined.
A.
B.
Data for analysis.......................................................................... Error! Bookmark not defined.
The estimates of regression model ............................................... Error! Bookmark not defined.
3
Introduction
Relevance of a subject of a research. Relevance of a subject of a research is defined by
need of introduction of effective models of the analysis for the oil and gas companies. It is caused
by implementation of large-scale investment projects in the field of geological exploration and
production, expansion of resource base and increase in a role of the Russian companies in the world
market. The growing interest of managers of the companies in innovative process in practice of the
analysis of data is important feature of modern economy. The need for active introduction to the
sphere of business and economic science of the questions connected with increase in efficiency of
the analysis of data is caused in our country by transition to market economy. Today adoption of
effective administrative decisions is connected with questions of the analysis of big data. The
indicator of growth of capitalization is the key indicator of effective management of the company.
Low market capitalization of the Russian oil and gas companies in comparison with the foreign
analogs comparable by the amount of business, causes the necessity of more detailed consideration
of the reasons of this disproportion and development of the effective methods of the analysis
directed to increase in capitalization of the companies.
The Russian oil and gas companies have limited potential on attraction of investments for
development of the business in view of low market capitalization. Considering the strategic
importance of an oil and gas complex, increase in effective management of data in the key Russian
companies is an urgent task for economy in general. Rates of development of branch reflect
dynamics of economic growth of the country in view of high interdependence with other branches.
Management of the Russian oil and gas companies is faced by a problem of improvement of
conceptual approaches to business management in the conditions of globalization of branch and the
increasing risks. Solid capitalization of the companies is a necessary condition for attraction of
investments, the loan capital, participation in large projects along with world leaders. In these
conditions the questions connected with identification of the factors defining capitalization of the
companies and a possibility of management of them.
Need of development of effective ways of company management and insufficient
understanding of factors of formation of capitalization cause the directions of a research of
dissertation work.
Now influence of corporate management, innovative activity and influence of financial
production factors on effective management of the company is insufficiently studied. In recent years
4
in such magazines as "Oil and gas business", "Economy questions", "Oil of Russia", "Oil, gas and
business", "Problems of economy and management of an oil and gas complex",
"HarvardBusinessReview", "The Times", "The Economist" has been published several articles
devoted to questions and problems of management of the oil and gas companies. At the same time
there is no complex research of influence of various factors on management process in the oil and
gas companies.
The research objective consists in development of an integrated approach to increase
capitalization of the oil and gas companies on the basis of the analysis of the most successful
world companies for the purpose of application of model in management of the Russian
companies.
For achievement of objective the following main goals are set:
1. To develop model of influence of operational and financial factors, allowing top
management to make decisions of development of the company for increase in capitalization.
2. To study innovative activity of the Russian and foreign oil and gas companies and to
define influence of major factors on efficiency of activity.
3. To develop recommendations about increase in capitalization by the vertically integrated
oil and gas company of Russia.
Research object – the largest public world oil and gas companies among which there are
10th Russian companies and 12 foreign companies are. The following factors became the main
criteria of the choice of the companies: existence of basic elements of vertical integration (upstream
and downstream), existence of the reporting under International Financial Reporting Standards. On
this group of companies statistical data on a financial and operational performance for 2013 - 2015
have been collected and analysed, the model of machine learning has been created further.
Subject of research are the factors defining market capitalization of oil and gas companies
operating in conditions of the developed world markets and uncertainty of long-term level of the
world prices.
Scientific novelty consists in improvement of the mechanism of the analysis of indicators
influence on capitalization and development the complex model of management decisions directed
to increase in capitalization of the oil and gas companies.
5
Research methods: In dissertation the method of the analysis of big data - machine learning
is presented to work. This method of the analysis provides big perhaps in the analysis of data, than
standard programs of a research. Machine learning rather new method of the analysis of data,
thereby this method have attracted great interest of the author. At application of machine learning
has been applied regression модлеь the analysis of panel data.
The model of machine learning of dependence of capitalization of the company on operating
rooms and financial factors is developed. This model allows to focus attention of managers in
management process on those factors which significantly concede in the importance to factors, the
foreign companies influencing capitalization. By the author it is defined that indicators of production
and reproduction of stocks aren't key for increase in capitalization of the Russian companies, and
for increase in capitalization of the domestic companies management needs to pay attention to
indicators of retail realization and processing of crude oil.
Recommendations which allow to provide higher level of capitalization of Russia which is
vertically integrated the oil and gas companies are offered.
Volume and structure of dissertation work.
Work consists of introduction, three heads, the conclusion, the bibliography (93 sources) and
appendices. The main text of dissertation work is stated on 50 pages.
In chapter 1 types of capitalization and the main characteristics of capitalization the
nefegazovykh of the companies of the world are considered. The main indicators influencing
capitalization of the companies are also considered.
In chapter 2 the description of the companies chosen for the analysis is submitted. theoretical
bases of the analysis of big data and a method of the analysis of big data are also presented.
In a chapter 3 creation of a method of a naliz is carried out. Creation of regressioynny model
about use of machine learning. Result of the carried-out analysis and recommendation of increase
in capitalization of the oil and gas companies of Russia.
6
Chapter 1. Capitalization and its role in development of the oil and gas
companies
1.1.
Concept and types of capitalization
In the modern literature on financial theory and practice, the concept of "capitalization" is
used quite often. It applies to companies with assets and, more recently, to the regions. The main
feature, which can be identified in the analysis of many works, is to give different meanings of these
concepts. In this case, the first step is to thoroughly investigate the possibility of using all we are
interested in the concept and in order to further study to combine these meanings, having formulated
the general concept of "capitalization of the company."
Most ordinary today meaning inherent in the concept of "capitalization" - it is the market
value of the company. So this concept is used in the context of works devoted to the issues of the
stock market. Usually, in order to clarify that we are talking about the capitalization in this sense,
the definition of "market capitalization". Here is just one of many similar definitions of market
capitalization: it is "the company's value, determined by the market price of its issued and
outstanding common shares" [1]. Often, the very definition of the market capitalization is replaced
by way of its calculation: the product of the market price of one share of the total number of shares
issued by the company [1; 2; 3].
DL Revutsky in the works [4; 5] makes an attempt to distinguish between the concept of
"value", "price", "cap" and "evaluation" of each other. As a result, it offers a similar interpretation
of the concept before us, "for joint-stock companies whose shares are traded on the stock market,
the real value of these businesses is considered to be equal to the stock (paper) the value of such
companies. As a synonym for the paper value of public companies almost always use the term
"capitalization" [5].
In the above quotation the author of, among other things, reduces the effect of this concept
only to public companies, thereby rejecting the existence of closed-cap companies, and the more
other objects.
The reduction of the concept of "capitalization" to the company's market value, the value of
which is calculated by the product of the exchange price of the shares on their number, inevitably
leads to the question of whether it is possible to talk about the company's capitalization, the shares
of which are not placed on the stock exchange, or even a non-stock company?
7
In the works of GI Khotyn [6; 7] There are several types of capitalization, but it is not given
a common definition. Firstly, it is considered a so-called "real cap", which arises from the increase
in shareholders' equity. If the company reinvests earnings, directing it to replenish current assets
and current assets, there is a real market capitalization, which is expressed in increasing the real
value of the property" [7].
It is worth noting that the accounting profit may not have the money form, and be, for
example, the result of the sale of goods on credit. In this case, the profit becomes a source of growth
financing receivables and capital gains, and moreover, the real capitalization, is not out of the
question.
In our opinion, the author made an attempt to express the idea that the real capitalization
occurs when two conditions are met. The company must produce value greater than it spends on its
production, and the added value to be reinvested in production, rather than spent. This understanding
of "capitalization" is based on the concept of "capital", formulated by Marx, which will be discussed
later [8].
Second, GI Hotinskaya highlights the so-called "marketing", or subjective, capitalization. It
happens, for example, by increasing the value of intangible assets (goodwill, trademark, "knowhow", etc.), the revaluation of fixed assets, which are the same as in the first case, entail the growth
of equity [3]. In other words, the difference between first and second modes is that in the real
capitalization of the company receives the financial resources at its disposal, which are used to
expand production, and in the second case, no financial resources there. The growth of the
company's value is due to the increase of property value through revaluation.
The main feature of the above interpretation of the two types of capitalization is a
representation of this concept by means of accounting terminology in conjunction with the balance
sheet of the company model. The author argues that the capitalization of the real "is reflected in the
third section of the balance sheet" and "leads to an increase in assets of the economic entity", and if
the marketing capitalization of the "increase in balance sheet ... comes originally from the asset"
[6].
For the third type of GI capitalization Hotinskaya gives the following definition: "the
capitalization - it is the market value of companies listed on the stock exchange, and is a product of
the market price of shares and the total number of shares of the company" [28]. This sense of
capitalization has already been considered above.
8
All three of the above type GI capitalization Hotinskaya attaches to the carrying model of
the company. We believe that linking the value of the capitalization of the accounting records is not
quite correct and narrows the concept itself, because not every change in the company's value can
be reflected in its balance sheet. Firstly, as already mentioned above, due to the specific accounting
methods, profits may not have cash filling and not lead to an increase in capitalization. Second, the
value of intangible assets rather difficult to accounting estimate and as such can be obtained
completely different values depending on the assessment method [11].
Capitalization in the sense of the product share price by the number generally can not be
linked to the balance of the account, but in the work of this author states the following: "The increase
in the market value of the shares and the value of the company as a whole is reflected in this case
(in the case of handling the company's shares on the stock exchange - DA) in the asset balance of
the revaluation of financial investments and liabilities balanced in additional capital "[28]. In the
quotation there is an obvious inaccuracy, because as financial investments is recognized in the
balance sheet assets do not own shares and shares of other economic entities. The change in the
market value of the treasury shares will not be reflected in the balance sheet of the company nor on
the part of the asset or liability side as exchange circulation of the company is completely divorced
from its operations.
Another context in which used the concept of "capitalization" is the capitalization of the
banking sector. The works on this subject, capitalization is considered as a build-up of commercial
banks, the equity due to the influx of additional financial resources [8; 9].
The tax practice of recent years used the term "thin capitalization", meaning financing
companies through intercompany loans, bringing the share of equity in the company substantially
reduced, and the interest on the loan obtained understate taxable income. The main objective of this
relationship is the transfer of profits under the guise of interest in the company that provided the
loan [10].
The above approaches to the "capitalization" understanding have one thing in common. It
consists in the fact that the cap appears as a quantity, which can be defined in different ways (either
through exchange of stock prices, either by accounting). That is, the market capitalization - a
measure, which can be characterized by a company in a particular point in time.
Fundamentally different interpretation of "capitalization" of the concept is present in the
works of T. Malova [11; 68]. There cap called a "fundamental process, the economic meaning of
which is to increase the cost of capital owned entities at all levels of management, as a result of the
9
growth of their economic capacity and effectiveness" [11]. The main feature that distinguishes this
interpretation of the concept of capitalization of the others, is to present it as a process. This is very
important because in all previous work in either explicitly or implicitly capitalization was
considered only as a state or company characteristics.
A similar view is also present in the works of SB Chernysheva: "Capitalization - both process
and is characterized by its integrated economic indicator. It refers to the ability of the financial asset
involved in the production of new value "[12; 47].
This approach to the interpretation of the concept of "capitalization", in our view, has its
roots in the works of Karl Marx, who wrote that the surplus value is capitalized, so it's capital: "...
under normal conditions, one part of the surplus value always shall be used as a income, and other
part - capitalized "[13]. In the second volume of "Capital" found many references to the
capitalization of surplus value is in the sense of turning it into capital. Of course, Marx did not
introduce the concept of "capitalization" and used the term only to refer to the cost of the
transformation process in the capital. But the processuality shade here is fundamental and gives
reason to use the concept of "capitalization", not only in the sense of "state", but also in the sense of
"process."
Regarding the concept of "capitalization", there are unresolved polls, such as the possible
existence of market capitalization in the conditions of absence. SB Pereslegin, for example, speaks
of the non-market capitalization "brand KGB" or "Mona Lisa" [14].
Thus, it turns out that the cap on the one hand, the condition, and on the other - a movement,
change. These two senses can be combined by using the category "process". Using the category
"process" implies a particular way of representing the object being studied, when, as some of its
characteristics are fixed state (the first point), and the object is presented as a sequence of changing
states (second meaning). More use of "process" category with respect to the capitalization will be
discussed in the next section.
What is the object appears as changing their status in the process of capitalization? It is
obvious that the object is included. Capitalization - a process of changes in value. The first, who
most consistently tried to explore the concept of value in economics, was Karl Marx.
According to Marx, the cost is a manifestation of human labor, or activity. "In direct contrast
to brute objectivity of trade bodies, is not included in the price no matter the nature of the atom. You
can feel and to look at every single item, do with it what you will, he as the cost remains elusive.
10
But if we recall that the goods have value only insofar as they are expressions of the same social
unity - human labor, that their value is therefore a purely social character, then we will be selfevident, that manifested it can only public in respect of one product to another "[15].
The meaning of the above phrase is that the cost is not an intrinsic property of things. Cost is the result of human effort, human labor. Marx is the value as an ideal object that can exist only in
the mind. The form of the manifestation of this ideal object is exchangeable value - proportion, "in
which one kind of use-values are exchanged for use-values of another kind" [36].
Marx notes that the exchange-value as such can not be an independent product property.
"The various exchange values of one and the same commodity express something equal, and
exchange value in general can only be a way of expression, a" form of expression "some great
content from it" [37]. This content is included. Marx shared together two concepts - "exchange
value" and "value", presenting the first as a second form of expression. The cost is presented as a
metaphysical entity of all goods. Exchange value is reflected in the ratio of exchange of different
products to each other.
Price has the property embodied in the form of the subject. The well-known formula "moneycommodity-money" illustrates the shift of subject forms rendered value. Money is the measure of
value, ie, in order to fix the value of the value embodied in different forms of subject, they should
be converted into cash. The cost of the Movement, in which it changes its form and subject to
enhance its value, Marx called the "capital", "the cost is ... self-propelled worth, self-propelled
money, and as such it is - capital. It emerges from the scope of treatment, re-enters into it, and stores
it multiplies itself, returns back and enlarged again and again starts the same circuit "[14]. The
property is self-movement of value, Marx explained by the device of human activity, in which
capitalists seek to get more and more money. And that, in his opinion, is only possible in the
implementation of the production of surplus value through the efforts of human labor.
In each specific situation as exchange goods with each other is not the cost, but the price.
Price is the monetary expression of value, embodied in the product of labor [14]. At the same time,
Marx notes that the price expresses the proportion of the exchange of goods for money, but this is
not an indicator of the magnitude of value.
Summarizing, we can conclude that Marx singled out the value as an object of thought. Cost
exists only due to human activities, in which it carries out its movement between various subject
forms. In each particular act of sale of goods cost is manifested in the form of the price of goods.
11
Later, in the works of neoclassical concept of value has been modified and dramatically
different from the one proposed by Karl Marx. A. Marshall points out: "Cost, ie, the exchange value
of a thing expressed in a certain place at a certain moment in terms of other things, represents the
number of units of the last things that you can get there and then in exchange for the former. Thus,
the concept of value is relative and expresses a relationship between two things in a particular place
at a particular time "[16; 11]. In this definition, there are several important points that distinguish
the concept of value in the direction of neoclassical economics (which began with the works of A.
Marshall) from Marxist.
The dependence of the value of time and place, its identification with the exchange value all this says about the failure to submit the price A. Marshall as a kind of "absolute immutable." On
the contrary, it becomes a situational, and therefore volatile and exposed to individual economic
agents. To establish the value of a thing should undertake action to exchange it for any other thing
(or money). That is, the cost is the result of a specific action. This A. Marshall identified the cost of
the price for Marx.
At the same time, such a move has been instrumental in the development of financial science.
Since cost is situational and in each case it can be different, it is possible to intentionally affect its
value either increasing or decreasing it. Once the price is not a reflection of the socially necessary
labor costs for production of goods, so there are other ways to measure the magnitude of value.
The popularity of neoclassical theory, in our view, given the freedom of subject forms of
value realization. If there is no specific reference to the labor costs, the cost can then enjoy and what
is not a product of human labor, or the value may be much higher labor costs in the manufacturing
of the product. Under this theory, it is logical and natural that the value of securities is much higher
labor costs incurred for their manufacturing. After the purchase and sale of securities that occurs on
a specific value, and identifies their value [36].
And, unlike the point of view of Marx, the value in neoclassical become procedural, ie,
changeable. During the continuous identification of the cost as a result of private transactions, its
value may be subject to constant change. The last statement reminds rotsessualnoe representation
capitalization. It can be said that the capitalization is just a process of constant change in the
magnitude of value as a result of specific human actions.
As a result of the emergence of new forms of subject realization value began to appear a
variety of types of capital (ie, self-expanding value). To better understand the "capitalization" of the
12
process to consider the concept and the basic types of capital that stand out in the economic and
financial science.
An indication of the capital, according to Marx, is that the value in the course of its
movement must constantly change shape. "If we fix some forms of which are increasing the cost
turns into its life circuit, then such determinations are obtained: Capital is money, capital is a
commodity" [17]. But capital is not identical to the forms that it takes. Movement thereon just need
to be able capital itself. That is, there is a capital cost of the process of movement in different forms
of subject. A cap is a process built over the capital and reflects the cost of the change embodied in
the substantive form.
1.2.
The main indicators of economic development of the countries and factors
influencing on the capitalization
The countries of BRICS increase the power in world economic and political arena. Today
BRICS is a quarter of world GDP (at par purchasing power), 44% of the population of the planet,
more than 30% of the territory of the terrestrial land and 50% of a gain of world GDP for the last
decades [50]. All countries of the block except for Russia have steadily positive rate of economic
growth.
In a pursuit of profit, investors gradually change the center of the interests, partially
transferring it to emerging markets which have bigger profitability, but are accompanied by high
risk.
Despite visible power, in political and economic spheres of the countries of BRICS there are
a lot of not coherences which are caused by various climatic conditions, culture, religion and other
powerful factors. Besides the countries of BRICS are subject to influence of various processes
proceeding in the developed countries such as change of a condition of the stock markets, the size
of demand for export goods, investments, exchange rates of currencies, etc.
The "western" concepts of functioning of the stock market and the capital markets, in the
countries with the developing and highly volatile market it is possible to apply, only considering a
set of restrictions. These concepts in relation to emerging markets are insolvent that can be caused
by a set of the reasons:
•
underdeveloped market infrastructure;
•
low level of protection of the rights;
13
•
poor quality of market information;
•
insufficient liquidity of securities market;
•
poor development of institutes of collective investment, owing to what in the market
few long-term investors and a large number of speculators.
Now the number of researches about interrelation of the foreign exchange and stock markets
under the influence of price of oil increases, but in the majority they are devoted to the developed
countries.
Jones and Kaul (1996) [42] used quarterly data for a research of dependence of the
international stock markets (Germany, Japan, Great Britain and the USA) and prices of oil. Using
model of vector regression, they have revealed the return interrelation, and especially strongly she
is shown in the markets of Germany and the USA. The markets of Japan and Great Britain react not
so actively.
Apergis and Miller (2009) [46], have applied the SVAR model, for the purpose of the
analysis of dependence between structural changes of the oil market and stock prices in the
developed G7 countries. Authors consider that sharp fluctuations in the market of oil don't exert
powerful impact on stock prices in the considered countries.
Hammoudeh (2004) [40] I investigated interrelation between price of oil and on an action of
Gulf States (Bahrain, Kuwait, Oman, Saudi Arabia and the UAE) and I have come to a conclusion
that only the stock market of Arabia has bidirectional communication between prices of oil and on
an action. Unlike the developed countries the interrelation between the price of oil and the stock
markets of developing countries is a little investigated.
The foregoing suggests that the capitalization process is associated with a variety of
mechanisms and carry out the movement of value, changing and fixing of its value. Among these
mechanisms play a significant role financial mechanisms.
14
1.3.
Key characteristics in influencing on the capitalization of a state and prospect
of development of the world oil and gas market
Oil still takes a leading place in a world energy balance and strengthens modern realities of
the energy market in which the countries having hydrocarbonic resources are large players the world
markets, and also often have advantages under the authority of the international relations.
The oil and gas sector has an important strategic importance for the Russian economy. At
the expense of the oil income the considerable share of GDP is created and macroeconomic stability
is maintained.
Despite fluctuations of demand for oil in the world markets, the volume of investigation
and drilling of new wells steadily increases [48]. In fig. 1. the size of world explored reserves of
oil for the last thirty years is displayed. About 13% of world reserves of oil [51] fall to the share of
Russia. Other countries members of BRICS totally have a share less than 5%.
1800
1600
1400
1200
1000
800
600
400
200
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
Fig. 1. - The size of world explored reserves of oil in 1980-2012, one billion bars
Source: Stаtisticаl Rеview of Wоrld Enеrgy, 2015
Many statistical data on the oil market rather disputable as various sources (OPEC, statistical
offices of the countries, the oil companies) provide information on the basis of application of
different methods of an assessment. Besides, results of calculation of the proved oil reserves in some
countries can change because earlier inefficient fields can be requalified in effective from the
economic point of view.
15
From table 1. it is visible that in the last decades exploration and production of oil has
increased more than by 2,5 times. The main consumers of oil in the world are China and the USA
which consume respectively 10,7 [52] and 18,87 million barrels daily [53]. The majority of
economic forecasts predict the further systematic growth of exploration and production of oil and
gas, despite the made progress in environmentally friendly power and mechanical engineering.
If to bring together statistics on production, stocks, processing and consumption of oil
products in regions of the world, then we will receive the following table 1.
Table 1. - Shares of regions of the world in production and oil refining for 2014
World's region
Middle East
South and Central
America
North America
Europe and
Eurasia
Africa
Asia-Pacific
region
Total
Share in oil
Share of
Share in oil
Share of
upstream , %
reserves, %
downstrem, %
32,5
48,4
8,9
9,1
9,2
19,7
6,4
7,3
17,5
13,2
22,8
24,6
20,3
8,4
25,8
21,3
10,9
7,8
3,6
4
9,6
2,5
32,6
33,6
100
100
100
100
consumption,
%
Source: Stаtisticаl Rеview of Wоrld Enеrgy, 2014.
Oil production grows at sure rates compared with the USA and Saudi Arabia in Russia. In
2014 she has grown up more than 1% in relation to the level of 2013 and has made 522,8 million t.
[54]. Increase in production in 2015 also was at the level of 1-1,5 percentage points. In fig. 2. it is
shown how oil production volume in Russia during 2007-2015 changed.
16
540
530
520
510
500
490
480
470
460
2007
2008
2009
2010
2011
2012
2013
2014
2015
Fig. 2. Dynamics of oil production (one million tons) in Russia during the period from 2007 to
2014.
Source: Rosstat
During the post-crisis period up to 2014 growth of oil production with rate of 1-2,5% was
observed that corresponded to expected values. It has been caused, first of all, by the high world
cost of oil which almost didn't fall lower than 88 dollars for barrel, since 2009, and a favorable trade
environment. Despite the strong decrease in oil quotations which has begun in 2014 Russia hasn't
reduced, and on the contrary, I have continued to increase extraction of oil products. But if not to
increase oil production growth at least to 5-7% a year, then Russia can concede superiority in this
branch of Saudi Arabia. So far Russia advances on production Arabia by 1 million barrels of oil a
day [55].
In the last decades in oil and gas sector the saved-up problems are aggravated. High cost of
production which is caused by remoteness from end users and adverse climatic conditions, and also
depth of deposits belongs to these problems. We will dwell upon some of them.Износ основного
капитала в сфере добычи нефти и переработки составляет уже более 80%, что объясняется
использованием устаревшего оборудования и отсутствием достаточного уровня частных
инвестиций в его обновление и замену.
Lack of development and mass introduction of innovative technologies doesn't allow branch
to develop successfully. The oil and gas companies are content with that level of profitability which
to them is brought by the available technologies. At the time of the Soviet Union of innovations in
branch divisions of the Ministry of oil industry promoted. Today, similar structures are absent, and
the money made in technological and scientific research doesn't pay off in the short term. Possibly,
17
in modern realities, only the help of the state, is capable to influence current situation - to provide
tax benefits that it became favorable to oil and gas companies to research and introduce innovative
developments that is unconditional, won't get approval among the population of Russia if to consider
that tax burden which is the share of small and medium business in our country.
In practice, in a pursuit of profit the domestic oil companies work not effectively that leads
to the high level of environmental pollution which is observed at all stages of production and
processing. Such consequences of low environmental friendliness as emissions of fuel evaporations,
emissions of harmful substances when transporting are observed, subsidence of terrestrial breeds.
Now Russia considerably lags behind other oil-producing and oil processing countries in
environmental issues of production and oil refining.
Production of the Russian oil and gas complex has not the highest quality, Russia exports
very few oil products with high value added. Oil refineries still continue to produce rather cheap oil
products, in comparison with the European standards that reduces the potential of export revenue.
Such tendency is observed because the majority of cars is specialized in Russia under consumption
of low-grade fuel that supports its production.
In the countries with more developed, great industry a situation other, by different estimates
is made by 72-77% of light products, for example the USA develops about 415 l from one ton of
crude oil. gasoline, Russia only 150 l. The generalizing Nelson coefficient is efficiency of
manufacturing industry in the United States makes 11 bps, in the countries of Europe on average 9
B. п, in the Russian Federation 4.2 bps. Practically all oil-producing countries actively are engaged
in processing of crude oil, all oil refineries are completely loaded. So, Saudi Arabia increases
capacities to 82%, the United States process twice more oil, than get in the territory, in the People's
Republic of China about one hundred oil refineries at which it is processed all got, and also import
oil [46].
However the last decade the government of Russia has taken a number of measures for
improvement of the current position of branch, entering new production schedules which promote
decrease in demand for low-grade fuel.
High level of consumption of energy and in comparison with other countries strongly
increases expenses and cost of products of oil and gas sector of Russia. In technological processes
more resources necessary for production or processing of the same amount of raw materials are
consumed. Use of outdated and imperfect technologies promotes excess of level of consumption of
energy by 1,5-2,5 times in comparison with the western analogs [54].
18
One more key negative moment connected with oil and gas branch of Russia is a high degree
of dependence of an exchange rate of ruble on oil cost. The Russian currency isn't freely convertible
that causes lack of an opportunity to export energy carriers for Russian rubles.
Now, oil products are trading in the world market in US dollars, and the profit on oil export
strongly correlates with an exchange rate of currency. When strengthening US dollar of receipt in
the budget from export of oil increase in a ruble equivalent, and at increase in an exchange rate of
the Russian currency the size of export revenue decreases on condition of preservation of volumes
of production and export by invariable. At fluctuations of an exchange rate even unprofitability
cases when exporting oil and oil products are possible. And as the proceeds from export of oil are
one of the main revenues of the budget of Russia, dependence on exchange rate endangers all
economic situation of the country.
The paradox of the oil and gas companies of Russia consists in their rather low capitalization.
The size of the underestimated assets in Russia continues to remain extremely high that is not logical
if to consider dynamics of oil production in Russia in recent years and a ratio of stocks at the key
Russian and international companies. This paradox can speak a variety of reasons, connected with
poor quality of level of corporate management, such as formalism and pretentiousness when not
appropriate compliance to the existing standards of corporate management is carried out. Also the
gap between strategic management and operational management at which strategic transformations
begin and come to an end at the level of top management concerns to them, mentioning the following
level of managers and specific experts a little, devaluating solutions of top management. Besides,
the used personnel management systems don't allow to use the personnel capacity of the companies
fully.
19
Chapter 2. Methodological bases of big data and machine learning
2.1.
Big Data. What it is and why it matters?
Big data is a term that describes the large volume of data – both structured and unstructured
– that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important.
It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead
to better decisions and strategic business moves.[60]
While the term “big data” is relatively new, the act of gathering and storing large amounts
of information for eventual analysis is ages old. The concept gained momentum in the early 2000s
when industry analyst Doug Laney articulated the now-mainstream definition of big data as the
three Vs:

Volume. Organizations collect data from a variety of sources, including business
transactions, social media and information from sensor or machine-to-machine data. In the past,
storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the
burden.[61]

Velocity. Data streams in at an unprecedented speed and must be dealt with in a
timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of
data in near-real time.[62]

Variety. Data comes in all types of formats – from structured, numeric data in
traditional databases to unstructured text documents, email, video, audio, stock ticker data and
financial transactions.[63]
Main types of analysis the big data:
What are the differences between data mining, machine learning and deep learning?
Although all of these methods have the same goal – to extract insights, patterns and
relationships that can be used to make decisions – they have different approaches and abilities.

Data Mining
Data mining can be considered a superset of many different methods to extract insights from
data. It might involve traditional statistical methods and machine learning. Data mining applies
methods from many different areas to identify previously unknown patterns from data. This can
include statistical algorithms, machine learning, text analytics, time series analysis and other areas
20
of analytics. Data mining also includes the study and practice of data storage and data manipulation
[62].

Machine Learning
The main distinction for the car studying - what in the same way as statistical models, the
purpose consists in understanding that the structure of data – corresponds to theoretical distributions
to data which are well understood. So, with statistical models there is a theory behind model which
is mathematically proved, but it demands that data have met certain strong assumptions also. The
car studying developed on the basis of ability to use computers to investigate data for structure even
if we have no theory of what that structure is similar to. The test for a machine learning model is a
validation error on new data, not a theoretical test that proves a null hypothesis. Because machine
learning often uses an iterative approach to learn from data, the learning can be easily automated.
Passes are run through the data until a robust pattern is found.[65]

Deep learning
Deep learning combines advances in computing power and special types of neural networks
to learn complicated patterns in large amounts of data. Deep learning techniques are currently state
of the art for identifying objects in images and words in sounds. Researchers are now looking to
apply these successes in pattern recognition to more complex tasks such as automatic language
translation, medical diagnoses and numerous other important social and business problems.[67]
Big data will become a key basis of competition, underpinning new waves of productivity
growth, innovation, and consumer surplus—as long as the right policies and enablers are in place.
There are five wide ways which use of big data can create cost. First, big data can unblock
the considerable value, doing information by the transparent and applicable on much higher
frequency. Secondly, as the organizations create and store more transactional data in the digital
form, they can collect more exact and detailed information of productivity concerning everything
from material resources of a product before sick days and therefore to provide variability and to
increase productivity. Leading companies use data collection and the analysis to make controlled
experiments to make the best administrative decisions; others use data for the main prediction of
low frequency to high-frequency nowcasting to correct their business levers just in time. Third, big
data allows ever-narrower segmentation of customers and therefore much more precisely tailored
products or services. Fourth, sophisticated analytics can substantially improve decision-making.
Finally, big data can be used to improve the development of the next generation of products and
21
services. For instance, manufacturers are using data obtained from sensors embedded in products to
create innovative after-sales service offerings such as proactive maintenance (preventive measures
that take place before a failure occurs or is even noticed) [65].
The use of big data will become a key basis of competition and growth for individual firms.
From the standpoint of competitiveness and the potential capture of value, all companies need to
take big data seriously. In most industries, established competitors and new entrants alike will
leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-realtime information. Indeed, we found early examples of such use of data in every sector we examined
[67].
The use of big data will underpin new waves of productivity growth and consumer surplus.
For example, we estimate that a retailer using big data to the full has the potential to increase its
operating margin by more than 60 percent. Big data offers considerable benefits to consumers as
well as to companies and organizations. For instance, services enabled by personal-location data
can allow consumers to capture $600 billion in economic surplus [75].
While use of big data will matter through sectors, some sectors are established for bigger
profit. We have compared the historical productivity of sectors in the United States to the potential
of these sectors to take cost from big data (the using index which unites several quantitative metrics),
and has found that opportunities and problems vary from sector to sector. The computer and
electronic devices and information sectors, and also finance and an insurance and the government
are ready to derive benefit significantly of use of big data.
There will be a shortage of talent necessary for organizations to take advantage of big data.
By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep
analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis
of big data to make effective decisions [87].
Several problems will have to be solved to receive the full potential of big data. The policy
connected with private life, safety, intellectual property, and even responsibility will have to be
addressed in the big world of data. The organizations have to not only put back the correct talent
and technology, but also both streams of operations of structure and incentives to optimize use of
big data. Access to data is crucial — the companies will have to integrate more and more information
from repeated data sources, is frequent from the third parties, and incentives have to exist to include
it.
22
2.2.
Machine Learning. What it is and why it matters?
Machine learning is a method of data analysis that automates analytical model building.
Using algorithms that iteratively learn from data, machine learning allows computers to find hidden
insights without being explicitly programmed where to look.[60]
Learning, like intelligence, covers such a broad range of processes that it is difficult to define
precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of,
or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by
experience.” Zoologists and psychologists study learning in animals and humans. There are several
parallels between animal and machine learning. Certainly, many techniques in machine learning
derive from the efforts of psychologists to make more precise their theories of animal and human
learning through computational models. It seems likely also that the concepts and techniques being
explored by researchers in machine learning may illuminate certain aspects of biological learning
[64].
As for machines, it is very wide that the machine studies every time when it changes the
structure, the program or data (on the basis of its inputs or in response to external information) in
such a way which is improved by its expected future operation. Some of these changes, such as
adding of the report to the database, fall conveniently in the field of other disciplines and aren't
surely best of all understood to be called a study. But, for example, when operation of the machine
of voice recognition improves after listening of several samples of the speech of the person, we feel
quite justified in this case to tell that the machine studied.
Machine learning usually refers to the changes in systems that perform tasks associated with
artificial intelligence (AI). Such tasks involve recognition, diagnosis, planning, robot control,
prediction, etc. The “changes” might be either enhancements to already performing systems or ab
initio synthesis of new systems. To be slightly more specific, we show the architecture of a typical
AI “agent” in Fig. 3. This agent perceives and models its environment and computes appropriate
actions, perhaps by anticipating their effects. Changes made to any of the components shown in the
figure might count as learning. Different learning mechanisms might be employed depending on
which subsystem is being changed [64].
23
Fig 3. the architecture of a typical AI “agent”.
Source: Christopher M. Bishop (2002–2006). Springer, 2006
One might ask “Why should machines have to learn? Why not design machines to perform
as desired in the first place?” There are several reasons why machine learning is important. Of
course, we have already mentioned that the achievement of learning in machines might help us
understand how animals and humans learn. But there are important engineering reasons as well.
Some of these are:
Some tasks can't be defined well except an example; that is, we could be able to define
couples of input-output, but not the short relations between entrances and desirable exits. We would
like that cars were able to adapt the internal structure to make the correct exits for a large number
of standard entrances and thus respectively to force their function of input-output to bring closer the
relations, implicit in examples.
24
It is possible that hidden among big heaps of data the important relations and correlations.
Methods of training of the car can often be used to take these relations (the analysis of data).
Human designers often make cars which don't work, and also wished in environments in
which they are used. Actually certain characteristics of a working environment couldn't be
completely known during design. Methods of training of the car can be used for improvement in a
workplace of the existing projects of the car.
The sum of knowledge available about certain tasks, could be too big for obvious coding by
people. Cars which study this knowledge gradually could be able to take more from it, than people
will want to write down.
Wednesday changes over time. Cars which can adapt to the changing conditions would
reduce the need for continuous modernization.
New knowledge of tasks constantly is people. Changes of the dictionary. There is a constant
stream of new events in the world. Continuation of modernization of the AI systems to correspond
to new knowledge is impractical, but methods of the car of studying could be able to track the most
part it.
Wellsprings of Machine Learning
Work in machine learning is now converging from several sources. These different traditions
each bring different methods and different vocabulary which are now being assimilated into a more
unified discipline.
Here is a brief listing of some of the separate disciplines that have contributed to machine
learning:
Statistics: A long-standing problem in statistics is how best to use samples drawn from
unknown probability distributions to help decide from which distribution some new sample is
drawn. A related problem is how to estimate the value of an unknown function at a new point given
the values of this function at a set of sample points. Statistical methods for dealing with these
problems can be considered instances of machine learning because the decision and estimation rules
depend on a corpus of samples drawn from the problem environment [67].
Brain Models: Non-linear elements with weighted inputs have been suggested as simple
models of biological neurons. Networks of these elements have been studied by several researchers
25
including [McCulloch & Pitts, 1943, Hebb, 1949, Rosenblatt, 1958] and, more recently by [Gluck
& Rumelhart, 1989, Sejnowski, Koch, & Churchland, 1988]. Brain modelers are interested in how
closely these networks approximate the learning phenomena living brains.
Adaptive Control Theory: Operate a research of theorists a problem of management of the
process having unknown parameters which have to be estimated at operation time. Often, change of
parameters during operation and process of control has to monitor these changes [76].
Psychological Models: Psychologists have studied the performance of humans in various
learning tasks. An early example is the EPAM network for storing and retrieving one member of a
pair of words when given another [Feigenbaum, 1961]. Related work led to a number of early
decision tree [Hunt, Marin, & Stone, 1966] and semantic network [Anderson & Bower, 1973]
methods.
Artificial Intelligence: Since the beginning the research AI concerned machine studying.
Samuel has developed the visible early program which has studied function parameters for an
assessment of provisions of board in a game in checkers [Samuel, 1959]. Researchers of AI also
investigated a role of analogies in studying [Carbonell, 1983] and as future activity and decisions
can be based on the previous model cases [Kolodner, 1993]. Recent work has been directed to
detection of rules for expert systems, using methods of a tree of decisions [Quinlan, 1990] and
inductive programming of logic [Muggleton, 1991, Lavra ˇ with & D ˇ zeroski, 1994]. Other subject
saved and generalized results of the solution of tasks, using the studying based on an explanation
[DeJong & Mooney, 1986, Laird, etc., 1986, Minton, 1988, Etsyoni, 1993].
Evolutionary Models: By the nature not only that certain animals learn to act better, but
also versions develop to be better, are located in their separate niches. As distinction between
development and studying can be washed away in computer systems, methods that certain aspects
of model of biological evolution have been offered as studying of methods to improve productivity
of computer programs. Genetic algorithms [Holland, 1975] and genetic programming [Koza, 1992,
Koza, 1994] are the most visible computing methods for evolution.
Learning Input-Output Functions
We use Fig. 4 to help define some of the terminology used in describing the problem of
learning a function. Imagine that there is a function, f, and the task of the learner is to guess what it
is. Our hypothesis about the function to be learned is denoted by h. Both f and h are functions of a
vector-valued input X = (x1, x2, . . . , xi , . . . , xn) which has n components. We think of h as being
26
implemented by a device that has X as input and h(X) as output. Both f and h themselves may be
vector-valued. We assume a priori that the hypothesized function, h, is selected from a class of
functions H. Sometimes we know that f also belongs to this class or to a subset of this class. We
select h based on a training set, Ξ, of m input vector examples [87].
Fig.4. - An input-output function.
Source: Christpher M. Bishop (2002–2006). Springer, 2006
Types of Learning
There are two major settings in which we wish to learn a function. In one, called supervised
learning, we know (sometimes only approximately) the values of f for the m samples in the training
set, Ξ. We assume that if we can find a hypothesis, h, that closely agrees with f for the members of
Ξ, then this hypothesis will be a good guess for f—especially if Ξ is large.
Curve-fitting is a simple example of supervised learning of a function. Suppose we are given
the values of a two-dimensional function, f, at the four sample points shown by the solid circles in
27
Fig. 5. We want to fit these four points with a function, h, drawn from the set, H, of second-degree
functions. We show there a two-dimensional parabolic surface above the x1, x2 plane that fits the
points. This parabolic function, h, is our hypothesis about the function, f, that produced the four
samples. In this case, h = f at the four samples, but we need not have required exact matches. In the
other setting, termed unsupervised learning, we simply have a training set of vectors without
function values for them. The problem in this case, typically, is to partition the training set into
subsets, Ξ1, . . . , ΞR, in some appropriate way. (We can still regard the problem as one of learning
a function; the value of the function is the name of the subset to which an input vector belongs.)
Unsupervised learning methods have application in taxonomic problems in which it is desired to
invent ways to classify data into meaningful categories [75].
We shall also describe methods that are intermediate between supervised and unsupervised
learning.
Fig 5. A Surface that Fits Four Points
Source: Christpher M. Bishop (2002–2006). Springer, 2006
We might either be trying to find a new function, h, or to modify an existing one. An
interesting special case is that of changing an existing function into an equivalent one that is
computationally more efficient. This type of learning is sometimes called speed-up learning. A very
simple example of speed-up learning involves deduction processes. From the formulas A ⊃ B and
28
B ⊃ C, we can deduce C if we are given A. From this deductive process, we can create the formula
A ⊃ C—a new formula but one that does not sanction any more conclusions than those that could
be derived from the formulas that we previously had. But with this new formula we can derive C
more quickly, given A, than we could have done before. We can contrast speed-up learning with
methods that create genuinely new functions—ones that might give different results after learning
than they did before. We say that the latter methods involve inductive learning. As opposed to
deduction, there are no correct inductions—only useful ones [75].
2.3.
Description of the companies of oil and gas companies
For the analysis 12 foreign and 10 domestic companies which are the largest companies in
the world on market capitalization have been chosen.
Foreign companies
We will consider the foreign vertically integrated companies and their indicators. Such
companies as "BritishPetrolium", "ExxonMobil", "Petrobras", "Total", "Chevrone" are analysed.
1)
"Chevron" is one of world leaders in the field of the integrated energy companies.
The success is based on aspiration to receive results from operating activities, application of
innovative technologies and to take new opportunities for profitable growth. Participates practically
on each aspect of power branch – in investigation, production and transportation of oil and natural
gas; production and sale of petrochemical production; power generation and productions of
geothermal energy; uses of renewables and power effective decisions; and development of fuel and
energy resources in the future, including researches in modern types of biofuel. In the world about
1 trillion barrels of crude oil have been let out today. Throughout the next century, approximately
in 2 trillion barrels it is more, than it is expected, have to be made of the usual proved reserves and
not explored reserves of oil. Additional deliveries will be made by the company "Chevron" and
other types of oil from nonconventional resources, such as high-viscosity heavy oil in Venezuela,
oil-bearing sand in Albert and slates in the USA.
2)
"ExonMobil" is the world's largest publicly traded the international oil and gas
company. Takes the leading place in branch of world resources of oil and gas. Is the world's largest
processor and marketing of oil products, the chemical company is among the world's largest. Break
of technologies, including, developed by the ExonMobil company - have helped to go in step with
growth of world demand for energy carriers by introduction more energy, expendables, and also to
decrease in impact on environment of power development. Today it is more important than
technology, than ever as the considerable part of world resources of oil and gas is in difficult
29
conditions, such as deep-water conditions, heavy oil / oil products, gas in dense breeds and the
Arctic regions which demand innovative approaches for energy production.
3)
"Total" is one of leaders of distribution of fuel in Western Europe and in Africa, and
also sells a wide range of products, dishes almost in 160 countries. Strategy of Group which carries
out the activity in more than 130 countries of the world and unites 97 000 employees, has the
purposes:
•
growth of his activity, exploration and production of hydrocarbons, and
strengthenings of the position in the world market one of leaders in the markets of natural gas and
LNG;
•
gradual extension of the offer of energy, accompanying growth of new power
sources;
•
adaptations of oil processing and petrochemistry on changes of the market, being
guided by several big competitive programs and maximizing profit of integration;
•
development of the activity of distribution of oil products, in particular in Africa, in
Asia and in the Middle East, keeping competitiveness of the operations in the mature markets;
•
continuation of intensive efforts for development of power sources "own" to make
the contribution to delay of demand for energy carriers and to take part in fight against climate
change.
4)
"British Petroleum" provides to clients for transportation of fuel, energy for
receiving heat and light, lubricants. Projects and operations help to create workplaces, investments
and tax revenues in the countries and communities worldwide. At each stage of hydrocarbonic
chains there are opportunities to create value - as as a result of successful performance of activity
which are key for branch, and by means of use of own distinctive advantages and opportunities in
implementation of these actions. The company aspires to add value at each stage of activity,
beginning from production and finishing with marketing. The company considers that to work on a
full hydrocarbonic chain of value creation it is possible to create additional cost for shareholders.
Integration also allows to develop the general functional best practices in such areas as safety and
operational risk, ecological and social practice, purchases, technology and management of money
more effectively.
5)
"Petrobras" - the Brazilian state oil company. The headquarters of the company is
located in Rio de Janeiro. In December, 2009 "Petrobras" has set a new record of daily oil production
in Brazil - 2 000 238 barrels. Only eight companies in the world every day make 2 and more than
million barrels of oil. In 2007 "Petrobras" has put into operation six new oil platforms with a total
power of 590 000 barrels of oil a day. At 16 oil refineries the company daily makes 1,839 million
barrels of oil products. Products of processing are on sale on 6933 gas stations from which 766 is in
30
property of Petrobras. 3 plants on production of fertilizers annually make 1,852 million metric tons
of ammonia and 1,598 million tons of urea. The established electric power of power plants of the
company for 2011 made 5000 MW Brazil makes about 35% of world ethanol. Petrobras as the state
company exports ethanol for use as automobile fuel. Ethanol is delivered to Venezuela and Nigeria.
The company conducts negotiations on supply of ethanol to China, to Yu.Korey, India and the USA.
"Petrobras" invested 330 million US dollars in 2010 in development of transport infrastructure of
ethanol.
6)
China Petrochemical Corporation (Sinopec Group) is a super-large petroleum
and petrochemical enterprise group, established in July 1998 on the basis of the former China
Petrochemical Corporation. Sinopec Group is a state-owned company solely invested by the state,
functioning as a state-authorized investment organization in which the state holds the controlling
share. Headquartered in Beijing, Sinopec Group has a registered capital of RMB 231.6 billion. The
board chairman of Sinopec Group is its legal representative.
Sinopec Group executes the investor rights over related state assets owned by its full
subsidiaries, controlled companies and share-holding companies. These rights include receiving
returns on assets, making major decisions and appointing management teams. The Group operates,
manages and supervises state assets according to related laws, and shoulders the corresponding
responsibility of maintaining and increasing the value of state assets. China Petroleum and Chemical
Company (Sinopec Corp.), controlled by Sinopec Group, issued H-shares and A-shares at overseas
and home respectively in October 2000 and August 2001 and was listed on stock markets in Hong
Kong, New York, London and Shanghai.
Sinopec Group’s key business activities include: industrial investment and investment
management; the exploration, production, storage and transportation (including pipeline
transportation), marketing and comprehensive utilization of oil and natural gas; the production,
marketing, storage and transportation of coal; oil refining; the storage, transportation ,wholesale and
retail of oil products; the production, marketing, storage, transportation of petrochemicals, natural
gas chemicals, coal chemicals and other chemical products; the exploration, design, consulting,
construction and installation of petroleum and petrochemical engineering projects; the overhaul and
maintenance of petroleum and petrochemical equipments; the research and development,
manufacturing and marketing of electrical and mechanical equipments; the production and
marketing of electricity, steam, water and industrial gas; the research, development, application and
consulting services of technology, e-business, information and alternative energy products; the
import and export of self-support and agent commodity and technology; foreign project contracting,
invite bidding, labor export; the international storage and logistics business.
31
7)
Royal Dutch Shell plc (Shell) is an independent oil and gas company. The Company
explores for crude oil and natural gas across the world, both in conventional fields and from sources,
such as tight rock, shale and coal formations. The Company is engaged in the principal aspects of
the oil and gas industry in approximately 70 countries. The Company operates in three segments:
Upstream, Downstream and Corporate. The Company's Upstream segment focuses on exploration
for new crude oil and natural gas reserves and on developing new projects. Its Downstream segment
focuses on turning crude oil into a range of refined products, which are moved and marketed around
the world for domestic, industrial and transport use. The Company sells various products, which
include gasoline, diesel, heating oil, aviation fuel, marine fuel, liquefied natural gas (LNG) for
transport, lubricants, bitumen and sulfur. It also produces and sells ethanol from sugar cane in Brazil.
8)
PetroChina Company Limited (“PetroChina”) is the largest oil and gas producer
and distributor, playing a dominant role in the oil and gas industry in China. It is not only one of the
companies with the biggest sales revenue in China, but also one of the largest oil companies in the
world
Since the foundation, PetroChina has established and improved standard corporate
governance structure, in accordance with the applicable laws and regulations including the
Company Law and the Mandatory Provisions for the Articles of Association of Companies to be
Listed Overseas and the Articles of Association. The shareholders’ meeting, the Board of Directors
and the Supervisory Committee of the Company can operate independently and effectively in
accordance with the Articles of Association.
PetroChina commits itself to becoming an international energy company with strong
competitiveness and one of the major producers and distributors of petroleum and petrochemical
products in the world. It engages in wide range of activities related to oil and natural gas, including:
exploration, development, production and marketing of crude oil and natural gas; refining,
transportation, storage and marketing of crude oil and oil products; the production and marketing of
primary petrochemical products, derivative chemicals and other chemicals; transportation of natural
gas, crude oil and refined oil, and marketing of natural gas.
PetroChina, under the guidance of the concept of scientific development, is dedicated to
implementing three strategies of resources, markets and internationalization. PetroChina is
committed to accelerating the transformation of economic growth, improving the self-innovation
capacity, establishing long-efficient mechanism of safety, environmental protection and energy
conservation and creating a harmonious enterprise, in order to transform itself to an international
energy company with strong competitiveness.
32
China National Petroleum Corporation (CNPC) is the sole sponsor and controlling
shareholder of PetroChina. It is a large petroleum and petrochemical corporate group, established
in July 1998, in accordance with Plan for the Organizations Structure Reform of the State Council.
CNPC is a large state-owned enterprise managed by the investment organs authorized by the state
and State-owned Assets Supervision and Administration Commission.
9)
ConocoPhillips explores for, produces, transports, and markets crude oil, bitumen,
natural gas, liquefied natural gas, and natural gas liquids worldwide. Its portfolio includes resourcerich North American tight oil and oil sands assets; lower-risk legacy assets in North America,
Europe, Asia, and Australia; various international developments; and an inventory of conventional
and unconventional exploration prospects. The company was founded in 1917 and is headquartered
in Houston, Texas.
10)
Eni engages in oil and natural gas exploration, field development and production,
mainly in Italy, Algeria, Angola, Congo, Egypt, Ghana, Libya, Mozambique, Nigeria, Norway,
Kazakhstan, UK, The United States and Venezuela, overall in 42 countries.
Eni sells in the European market basing on the portfolio availability of equity gas and longterm contracts; sells LNG on a global scale. Produces and sells electricity through gas plants.
Through refineries, Eni processes crude oil to produce fuels, lubricants that are supplied to
wholesalers or through retail networks or distributors. Eni engages in the trading of oil, natural gas,
LNG and electricity.
Integrity in business management, support the Countries development, operational
excellence in conducting operations, innovation in developing competitive solutions, inclusiveness
of Eni’s people and development of know-how and skills, integration of financial and non-financial
issues in the company’s plans and processes drive Eni in creating sustainable value.
11)
Statoil ASA is an energy company, engaged in oil and gas exploration and
production activities. The Company's segments include Development and Production Norway
(DPN), Development and Production USA (DPUSA), Development and Production International
(DPI), Marketing, Midstream and Processing (MMP), New Energy Solutions (NES), and Other.
DPN comprises upstream activities on the Norwegian continental shelf (NCS). DPUSA comprises
its upstream activities in the United States and Mexico. DPI develops and produces oil and gas
outside the NCS. MMP comprises marketing and trading of oil products and natural gas,
transportation, processing and manufacturing. NES is responsible for wind parks, carbon capture
and storage, as well as other renewable energy and low-carbon energy solutions. The Other reporting
33
segment includes activities in Technology, Projects and Drilling (TPD), Global Strategy and
Business Development (GSB), and Corporate staffs and support functions.
12)
Pemex are the most important company in Mexico and one of the largest in Latin
America, and our success is reflected in our dedication to the future of Mexico. As the largest tax
contributor to the Mexican government, the income Pemex generate helps support all three levels
of government: federal, state and municipal. Pemex directly and indirectly participate in the
economic and social development of our country. Pemex operations are geographically dispersed
throughout Mexico, and have presence in almost every state.
Through its long history, Pemex has become one of the few fully integrated oil companies,
developing our entire productive chain: exploration, production, industrial processing/refining,
logistics and marketing.
Logistically, Pemex have 83 land and maritime terminals, as well as oil and gas pipelines,
maritime vessels, and varying fleets of ground transportation in order to supply over 10,000 service
stations throughout the country.
Pemex business involves a complex myriad of installations and advanced technology, yet
the core of the company is its experienced and trained personnel. Pemex is its employees. Pemex
recognize the need to protect and invest in our most precious resource that is why Pemex seek to
continuously be a socially responsible company that works under stringent safety, job, health and
environmental protection standards.
Domestic companies
We will consider the main vertically integrated the companies, such as "Bashneft",
"Rosneft", "Lukoil", "Gazpromneft", "Tatneft", etc. We will submit for a start the short
characteristic of the vertically integrated companies in Russia.
1.
Bashneft - the integrated oil company which is dynamically developing vertically
created on the basis of the largest enterprises of energy industry of the Republic of Bashkortostan.
The company enters in top-10 the enterprises of Russia for the volume of oil production and in top5 - on oil processing. JSC ANK Bashneft - large diversified is production - a technological complex
of the Republic of Bashkortostan which activity covers all aspects of oil business from geological
exploration and production before marketing, production and product sales. The saved-up
production in the territory of Bashkiria makes more than 1,65 billion tons. Investigation and
development more than 160 oil and gas fields. Oil production more than 15 million tons of oil a
34
year. The company has shown the highest growth rates of oil production among VINK in 2009-11.
Powerful scientific potential is a long-term experience of development and deployment of advanced
technologies of exploration and production. The company has a complex from four modern and hitech oil processing enterprises. Processing makes about 21 million tons of oil a year. JSC ANK
Bashneft is the branch leader in oil refining depth with an indicator of 85,9%. The retail network
consists from more than 460 own and 220 partner gas stations. Investments into development of
social infrastructure of the Republic Bashkortostanv of 2009-2011 have made about 4 billion rubles.
2.
Rosneft – the leader of the Russian oil branch and one of the largest public oil and
gas companies of the world. Primary activities of "Rosneft" are exploration and production of oil
and gas, production of oil products and production of petrochemistry, and also is sold the made
production. The company is included in the list of the strategic enterprises of Russia. Its main
shareholder (69,50% of stocks) is JSC ROSNEFTEGAZ for 100% belonging to the state. About
10% of shares of the company are in free circulation.
3.
Lukoil - one of the largest international vertically integrated oil and gas companies
providing 2,2% of world oil production. The leading positions of the Company are result of twenty
years' work on expansion of resource base thanks to increase in scales of activity and the conclusion
of strategic transactions. JSC Lukoil realizes projects on exploration and production of oil and gas
in 12 countries of the world. The proved reserves of hydrocarbons of Lukoil group as of the end of
2011 make 17,3 billion barrels AD. 90,5% of the proved stocks of the Company and 90,5% of
extraction of commodity hydrocarbons are the share of Russia. Abroad the Company participates in
projects on oil and gas production in five countries of the world. The main part of activity of the
Company is carried out in the territory of four federal districts of the Russian Federation –
Northwest, Volga, Ural and Southern. Western Siberia of which 42% of the proved stocks and 49%
of extraction of hydrocarbons are the share remains the main resource base and the main region of
oil production of the Company. 9,5% of the proved stocks of the Company and 9,5% of extraction
of commodity hydrocarbons are the share of the international projects.
4.
Gazpromneft and her subsidiaries represent the vertically integrated oil company
(VIOC) which primary activities are investigation, development, production and realization of oil
and gas, and also production and sale of oil products. "Gazprom Neft" carries out the activity in the
largest oil-and-gas regions of Russia: Khanty-Mansi and Yamal-Nenets autonomous areas, Tomsk,
Omsk, Orenburg regions. The main refinery capacities of the company are in Omsk, Moscow and
Yaroslavl regions, and also in Serbia. Besides, the company realizes projects in the field of
production outside Russia - in Iraq, Venezuela and other countries.
5.
Tatneft - one of the largest domestic oil companies which is carrying out the activity
in the status of vertically integrated Group. About 8% of all extracted oil to the Russian Federation
35
and over 80% of the oil extracted in the territory of Tatarstan fall to the share of the Company.
Stocks of JSC Tatneft are included into group the most demanded at the leading Russian stock
markets: To the London stock exchange and in the German system of the Deutsche Burs of AG
group.
6.
"Surgutneftegas"
The main directions of business of the company are:

Exploration and production of hydrocarbonic raw materials,

Oil refining, gas and electricity generation,

Production and marketing of oil products, gas processing products,

Development of products oil and gas chemistries
The oil and gas extraction company "Surgutneftegas" - one of the largest enterprises of oil
branch of Russia.
For many years the enterprise is the leader of branch in prospecting, development drilling
and commissioning of new production wells.
At the enterprise the Russia's first full cycle of production, processing of gas, development
on his basis of own electric power, receiving a ready-made product is created. Structural divisions
of the enterprise carry out all complex of works on investigation and development of fields, on
building of production objects, on ensuring ecological safety of production and on automation of
productions.
Oil refinery of the company – "Kirishinefteorgsintez" - one of the largest oil processing
enterprises of the country.
The plant lets out oil processing products with high ecological and operational properties,
including motor fuels, aromatic hydrocarbons, liquid paraffin, roofing and waterproofing materials,
etc. The diesel fuel, aviakerosene, roofing materials and bitumens released by plant conform to the
international quality standards.
Kirishinefteorgsintez repeatedly received awards of the Government of the Russian
Federation and prestigious international awards for high quality of products.
7.
Russneft Oil Company - vertically integrated oil holding, is included into ten the
largest Russian oil companies. Its structure includes 24 the extracting enterprises, conducting the
36
activity in the territory of the Russian Federation and Belarus. In development the company has 167
oil and gas fields, total recoverable oil reserves of the company make over 600 million tons.
8.
Novatek from the moment of the creation the Company has concentrated the efforts
on development of oil and gas assets. Licenses for the fields located in the Yamalo-Nenets
Autonomous Area - East Tarkosalinskoye, Hancheyskoye, Yurkharovskoye have been acquired, and considerable means are invested in their development and arrangement. In 1996 trial operation
of oil trade of the East Tarkosalinsky field has been begun, and 1998 on the field the first natural
gas has been extracted. In 2002 with the first supply of gas to end users gas marketing development
has begun.
In the next years outputs and a portfolio of assets of the Company grew at the accelerated
rates. Shares in such joint ventures as "SeverEnergia", "Northgas", "Yamal LNG", and also new
licenses, including perspective sites on the peninsula Gydan and in the water area of Gulf of Ob
have been acquired. Implementation of the large-scale project on construction of LNG of Yamal
LNG plant which will allow the Company to enter the international market of gas is begun.
Development of the vertically integrated production chain which major stage was a start in 2013 of
a complex on processing of condensate in Ust-Luga has continued.
9.
"Slavneft" is included into ten the largest oil companies of Russia. Vertically
integrated structure of holding allows to provide a full production cycle: from investigation of fields
and production of hydrocarbonic stocks before their processing. "Slavneft" owns licenses for
geological studying of a subsoil and oil and gas production on 31 license sites in the territory of
Western Siberia (Khanty-Mansi Autonomous Okrug-Yugra) and Krasnoyarsk Krai.
"Slavneft" - the main oil-extracting enterprise of the Company is JSC SlavneftMegionneftegaz (JSC SN-MNG). Working on Megion, Agansky, Vatinsky, Taylakovsky and some
other fields, "SN-MNG" monthly extracts about 1,3 million tons of hydrocarbonic raw materials.
Annual production of all enterprises of holding makes about 15,5 million tons of oil.
The extracted oil (except for an export share) goes to processing which is carried out by JSC
Slavneft-YaNOS and JSC Mazyr Oil Refinery. The oil processing enterprises of the Company have
considerable production capacities and the modern equipment that allows to turn out high-quality
products at the level of the international standards. Annually Slavneft Oil Refinery is processed by
about 27,6 million tons of hydrocarbonic raw materials and make more than 5,4 million tons of
motor gasolines.
37
The business strategy realized by "Slavneft" is urged to provide the sustainable and balanced
development of oil-extracting and refinery capacities. The main objectives of the Company are
stabilization of level of oil production, continuation of modernization of processing industry and
accumulation of volumes of processing of raw materials, and also business restructuring, creation
of the optimum scheme of interaction of the enterprises of holding, decrease in expenses and growth
of efficiency of a production activity now.
10.
PAO "Gazprom" — the global energy company. The main activities — geological
exploration, production, transportation, storage, processing and realization of gas, gas condensate
and oil, realization of gas as motor fuel, and also production and sale of the heat and electric power.
"Gazprom" sees the mission in the reliable, effective and balanced providing consumers with
natural gas, other types of energy resources and products of their processing.
"Gazprom" has the richest reserves of natural gas in the world. His share in world gas
reserves makes 17%, in the Russian — 72%. 12% of world and 69% of the Russian gas production
are the share of "Gazprom". Now the company actively realizes large-scale projects on development
of gas resources of the Yamal Peninsula, the Arctic shelf, Eastern Siberia and the Far East, and also
a number of projects on exploration and production of hydrocarbons abroad.
For the analysis operational and financial results from 2013 for 2015 have been chosen.
Statistical data are presented in the appendix.
Table 2. - Indicators and their description, that using for analysis.
Indicator
Capitalization, mln $
retail, mln $
operatoin cost, mln $
Share of downstream to oil
production, %
Description
The total value of a company's shares on a stock
exchange
the activity of selling goods to the public
expenses associated with the maintenance and
administration of a business
Share of downstream to oil production
downstream, mln t
The volume of downstream activity
production, mln t
Amount of the extracted oil
38
The amount of gas or oil that is known to exist
proven reserves, mln t
under the surface of the earth or under the sea and
that is available for future use
export, mln t
The amount of oil and gas export
share of export
share of export to total production of oil and gas
gross profit, mln $
ROACE
company’s profit from selling goods or services
before costs not directly related to producing them
Return on Average Capital Employed
Source: development of the author.
39
Chapter 3. Development of a method of machine learning for increase the
capitalization of the oil and gas companies of Russia.
3.1.
Sequence of a research.
In a research the author suggests three main hypotheses which are checked further
3 hypothesis
Results
1 – whether operational indicators influence
the oil and gas companies
2 – Downstream has influence on
capitalization of the oil and gas companies;
3 - Export of oil and gas has influence on
capitalization
1 – confirmed. Indicators of
operating activities also have
influence on capitalization of the oil
and gas companies
2 - confirmed. The foreign
companies have big capitalization
because downstream more
developed.
3 – wasn’t confirmed.
Fig 6 – stage of the analysis.
Source: development of the author.
The first hypothesis consists that now at the accounting of capitalization of the company
only financial performance is considered. The author checks this hypothesis. the second hypothesis
consists in what the Russian companies pays not enough attention to oil processing and exports
crude oil much that also the author will be checked in thirds to a hypothesis.
Now many companies look for possibilities of the analysis of the big data which are saved
up during existence of the company. The most developing directions of the analysis of big data is a
machine learning.
40
This direction of the analysis of data for a long time ago it was offered, but there were no
such computing capacities earlier. Modern technologies have made possible applications of machine
learning.
For the analysis of big data the author has chosen a method of machine learning in
precedents.
Stages (fig. 7.) of creation of model of machine learning in the following:
Formation of the database for carrying out the analysis
To study the main methods of the analysis of big data
Creation of an algorithm of calculation of machine
learning
The analysis of the received results
Results and recommendationResults and
recommendation
Fig 7 – stage of the analysis.
Source: development of the author.
1)
Formation of the database for carrying out the analysis;
All data were collected from open sources, annual reports. The author collected annual data
for 3 years. It is the small database. It was made, because of computer have small power.
The large companies are able to afford to apply various methods of the analysis of data. For
the analysis of big data big settlement capacities of the car are required. Also the companies at
application of machine learninig can with be pushed with a problem of incorrect data storage that
will promote to the solution of this problem and will lead to the best analysis of data if they even
don't begin to use machine learning.
41
2)
To study the main methods of the analysis of big data;
For the analysis of big data the author suggests to use method of machine learning with the
teacher. As the method given a method the most suitable for the purpose of dissertation work.
3)
Creation of an algorithm of calculation of machine learning;
For creation of machine learning the Python programming language as it is a simple language
for studying has been chosen and doesn't demand deeply programming studying.
Python is simple to use, but it is a real programming language, offering much more structure
and support for large programs than shell scripts or batch files can offer.
Stages of machine learning:
a)
We consider the leading world oil and gas companies;
b)
we build regression model and we study as the chosen indicators influence
capitalization of the foreign oil and gas companies;
c)
we build regression model and we study as the chosen indicators influence
capitalization of the domestic oil and gas companies;
d)
We learn the machine, so that it gave us the answer to that what indicators differ more
than for 30%
4)
The analysis of the received results.
After creation of an algorithm of machine learning, we carry out the analysis on adequacy
of the obtained data with the help of an assessment of regression model.
Example of the model of machine learning written by the author is presented in the following
section.
This algorithm needs to be improved under each company or a situation. As the companies
have the specific works and various directions of application. Also the companies can expand this
functionality which will look not only at the chosen factors. The companies can apply a method of
machine learning from the general to the particular. That is to apply the offered method to the
general strategy, and then to expand model and to apply specifically on an opredenny factor.
Thereby the companies will be able to unite all constructed models in one which will show activities
to which it is necessary to pay attention. So top management will be able to capture many fields of
activity and to make decisions.
42
3.2.
Application of machine learning for the analysis of dependence of capitalization
on various factors of activity of the oil and gas companies
Creation of machine learning requires knowledge of the Python programming language. As
it was told earlier, this programming language is easy for machine learning and application.
For writing of a script we will use the Anaconda program. This platform is convenient that
it has a big volume packages of data for programming.
Anaconda is the leading open data science platform powered by Python. The open source
version of Anaconda is a high performance distribution of Python and R and includes over 100 of
the most popular Python, R and Scala packages for data science.
Additionally, you'll have access to over 720 packages that can easily be installed with conda,
our renowned package, dependency and environment manager, that is included in Anaconda.
Also for writing of a code it is necessary knowledge of the main libraries such as:
1)
package of numpy contains realization of multidimensional massifs and algorithms
of linear algebra.
2)
The pandas package provides a wide range of functions on processing of tabular data.
3)
The scikit-learn package realizes a set of algorithms of machine learning.
First step. We load packages in the Anaconda program in order that we could use
opportunities provided in these libraries.
43
Fig 8. Script.
Source: Development of the author.
Second step. Further we need to load our data given on a platform.
In the program recognizes different data types (.xls, .xlsx, .csv, .xml and others). For
convenience we will use permission of .csv. We select this document format for speed of data
loading and simplicity of perception of data of pdatformy.
Fig 9. Script.
Source: Development of the author.
Third step. We build regression model for the loaded data. As we have loaded data the
zaubezhnykh of the companies, the received results on each sign will be a vector of the correct
answers. It is useful to us at a further algorithm.
We carry out the correlation analysis
Fig 10.
Source: Development of the author.
Further we build regression model and we receive the weight of each parameter
44
Fig 11. Script.
Source: Development of the author.
Fourth step. We load data on domestic the company the same mechanism as in the second
step.
Fig 12. Script.
Source: Development of the author.
Fifth step. We carry out the regression analysis of the domestic companies according to an
algorithm described in the third step.
45
Fig 13. Script.
Source: Development of the author.
Table 2. Indicators importance
Indicator
Foreign
Domestic
Indicator
Foreign
Domestic
Retail
0,1
0,03
Proven reserves
0,1
0,13
Operatoin cost
0,1
0,09
Export
0,08
0,12
0,07
0,02
Share of export
0,03
0,15
Downstream
0,1
0,02
Gross profit
0,21
0,19
Production
0,09
0,15
ROACE
0,12
0,1
Share of downstream to
oil production
Sixth step. Then we compare importance of each parameter and we find factors which
importance differ more than for 30% for the worse.
46
Fig 13. Script.
Source: Development of the author.
After creation of a code of machine learning the name of indicators which differ more than
for 30% will be output.
Parameters of regression model it is presented to the appendix 2.
In dissertation work it is two indicators of a retail and downsteam of crude oil.
47
3.3.
Results and recommendations.
When using machine learning the author has received results that the Russian oil and gas
companies lag behind from foreign in development of retail trade and oil processing of crude oil
more than for 30%.
At the received results top management of the company can put emphasis on development
of these fields of activity that will exert impact on capitalization of the companies and attraction of
investments on development of the company generally.
The Russian companies strongly lag behind in capitalization in comparison with foreign
because they are less interested in work with the costumers of oil products.
For development of retail trade of the oil and gas companies the author suggests to develop
network of gas station with shops, on the example of PAO Gazprom gas Station. Also Rosneft tries
to develop gas station with shops in the countries of Europe. Retail trade not only in oil products,
but also convenience goods increases profitability of the company that will positively influence
capitalization of the company. Considering experience of the foreign companies which have big
networks of retail trade, the Russian companies will increase appeal of the company.
Downstream of crude oil will also increase capitalization of the company. At oil refining in
the company, will increase appeal of the company in the world market. It is well known that oil
costs much and recently only become more expensive. It is known as well the fact that oil processing
doubles income gained from oil. And the petrochemistry triples him. It will lead to attraction of new
investments for development of the company that by all means will affect capitalization of the oil
and gas company of Russia.
Therefore, development of retail trade and processing the company will allow to increase
capitalization and appeal of the company in the investment plan and trust from the costumers.
Now, most of top managers of the companies make the decision on the basis of analytical
data. The author has offered one of a set of solutions of making decision on development of the oil
and gas companies. At development of this model top managers can make decisions not only for
increase in capitalization, but also resolve other issues. If to develop the analysis of data in
channelized, then the oil and gas companies will be able to control a set of processes. Also oil and
gas companies will be able to expand model and to solve problems from the particular to the general.
It will lead to the fact that top managers will be able to see results of various fields of activity.
48
During creation of the general model of business, they will be able to reduce expenses and to
increase financial performance in a positive side.
Even application of machine learning or aspiration to the analysis of big data will be involved
new by the investor. As development in the analysis of data lets know to investors that the companies
are ready to develop together with development of modern technologies of the analysis of data.
49
Conclusion
Capitalization represents very difficult system of interrelations between political, economic,
and ecological spheres of life. However it isn't always possible to reach that situation when all these
spheres function it is adjusted with hardly any trouble at all. It is possible to call such model of
increase in a capitalization of an oil and gas company safely theoretical as in a real situation the oil
and gas companies have the bigger number of data. As for the oil and gas companies of the Russian
Federation, it one of those countries which by all means need to seek for increase in capitalization
of the oil and gas company. The domestic companies have high potential in development of spheres
of retail trade and processing of crude oil. In Russia there is a lot of highly skilled employees in this
sphere.
In this work the research during which the objectives have been achieved - to estimate
influence of factors which promote development of the oil and gas companies of Russia has been
conducted. First of all, the theoretical aspects connected with the general information on the analysis
of data, and also, in particular, about application of one of methods of the analysis of data have been
considered. An important stage of work is consideration of research works of foreign authors who
are engaged in studying of development of machine learning and capitalization of the oil and gas
companies. It is very important to understand what decisions on development of the company is
accepted on the basis of the analysis of big data.
At the following stage of operation it was extremely important to analyze tools of the
econometric analysis as by means of it the research objective was achieved. In particular, in
operation any creation of regression models with use of data of the panel type in an econometric
method is considered, such data type also was used in the conducted research.
However in it still there are no observations on the significant amount of objects and the
temporal period that complicated carrying out a research. The most adequate regression models are
built on the basis of a large number of data on the given subject. The regression model in this
operation says that the majority of variables isn't considered in operation as other indices are absent
in the database.
The research has been done in due form econometricians. Though the model has also been
checked, but carrying out a similar research, but already to other period and on bigger number of
data is in the future possible. Two researches can be compared among themselves, and then to carry
out comparison with similar foreign researches. Comparison will give an incentive to identification
50
of the reasons of distinction or similarity of indicators. Undoubtedly, it will promote accumulation
of excessively important experience in area of development of machine learning and increase in
capitalization of the oil and gas companies of Russia.
51
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