CASE STUDY: Optimisation Solutions in Offshore Oil and Gas
Minimum-Cost Vessel Scheduling in the Australian North West Shelf by Curtin University
PROJECT SNAPSHOT
Summary
Woodside Energy required a method for optimally scheduling
the cargo delivery and off-take operations of its support
vessels in the North West Shelf region of Australia.
Using advanced techniques in mathematical optimisation,
researchers from Curtin University developed scheduling
models that take into account a range of operational factors
including vessel speeds, vessel capacities, facility closures,
facility service durations, cargo demands and off-take support
requirements.
At a strategic level, the models were used to advise Woodside
on the best vessel routes and optimal fleet replacement
strategy; at a tactical level, the models constitute the basis for
an automated scheduling tool currently in development.
Background
Woodside Energy is Australia’s largest independent oil and
gas company with activities spanning Australia and the Asia
Pacific Region, the Atlantic Sea, Latin America and SubSaharan Africa. Woodside has over 60 years of production and
exploration experience and manages some of the world’s
premier liquefied natural gas (LNG) facilities.
Client: Woodside Energy Ltd
Sector: Oil and Gas
Company type: Public (ASX: WPL)
Number of Employees: approx. 3000
Revenue: $7.076 billion (2014)
Profit: $2.414 billion (2014)
Headquarters: Perth, WA
Website: www.woodside.com.au
Challenge
 Determine the times and sequence
at which support vessels should
visit offshore oil and gas facilities
to minimise operating cost
Deliverable
 A mathematical scheduling model
for generating optimal vessel
routes in any operating scenario
Impact
 Insight into the best vessel routes
and schedules, and optimal fleet
replacement strategy
 Reduction in current fleet from
four to three vessels, with a cost
saving of $10m/year across the
Woodside-operated assets
 Automated vessel scheduling tool
currently in development
Woodside operates eight offshore oil and gas facilities in the Australian North West Shelf – Angel,
Goodwyn, Nganhurra, Ngujima-Yin, North Rankin A, North Rankin B, Okha and Pluto. These facilities
require regular shipments of commodities such as general cargo, water, chemicals and fuel. The
shipments are performed by a fleet of support vessels operating out of King Bay Supply Base in
Karratha. The support vessels are also required to assist with oil off-takes, whereby oil is transferred
from an offshore facility to a waiting tanker.
Scheduling the cargo delivery and off-take operations of the vessel fleet is a critical issue affecting
business productivity.
Many factors and operational constraints must be taken into account when designing the best vessel
schedule — for example, vessel speeds and capacities, off-take equipment availability (not all vessels
have the equipment needed to assist with off-takes), restrictions on night-time loading and facility
closing times.
To develop an optimal scheduling method, Woodside engaged a research team at Curtin University
consisting of A/Prof Ryan Loxton, Dr Elham Mardaneh and Dr Qun Lin from the Department of
Mathematics and Statistics. Woodside employees Phil Schmidli, Nicola Wilson and Wayne Watson
were also involved in the project.
Challenge
Woodside’s cargo delivery and off-take operations are currently scheduled manually. Woodside was
investigating potential fleet replacements and new schedule formats, which required determining
the optimal schedule (and associated performance metrics) for a large number of realistic operating
scenarios. It was impossible to manually construct all of the required optimal schedules; a
computational scheduling model was therefore needed to automatically determine the minimumcost vessel routes for given cargo delivery and off-take requirements.
Deliverables
The Curtin research team developed mathematical scheduling models that could be implemented
and solved using optimisation algorithms. The models were designed to answer the specific
question: At what times, and in what sequence, should the vessels visit the different facilities in order
to minimise operating cost and ensure that all cargo delivery and off-take support requirements are
completed? The models were used to compare different schedule formats and different fleet
replacement options, and subsequently advise Woodside on the optimal choices.
Impact
The mathematical models played a key role in establishing a business case for reducing Woodside’s
current North West Shelf regional fleet from four to three vessels, with a corresponding charter cost
saving for the joint venture of around $10 million per annum. This work has led to two academic
journal publications with several others currently in preparation. In addition to the North West Shelf
regional fleet, the models were also applied to evaluate different fleet options for Woodside’s
flagship Browse floating LNG project.
The mathematical models from the Curtin team are now being further developed into a scheduling
tool for day-to-day tactical decision-making. This tool will allow Woodside to easily update the vessel
schedule in response to unforeseen circumstances such as cyclones and equipment breakdowns,
and determine the best vessels to hire when additional fleet capacity is required.
“Each model has added a new element to the decision-making toolbox, and has assisted greatly with
our planning. An extra benefit is that the models have built on each other, enabling faster and more
detailed personalized solutions to be delivered that are unique to our circumstances. The team at
Curtin have approached each project in an enthusiastic manner and delivered meaningful results for
our business.” -Rob Duncanson, Marine Manager, Woodside Energy Ltd
Key Learnings
This project showed that research collaboration in data science between academia and industry can
have a direct impact on business productivity and can be completed within commercial timeframes.
By exploiting the latest data analytics techniques (mathematical optimisation in the case of this
project), businesses can streamline their operations, lower operating costs and develop new
competitive edges.
The project also highlighted the importance of forming a strong team with multi-disciplinary
expertise in various areas of data analytics (modelling, optimisation, scientific computing and
process automation) together with practical engineering insight and knowledge. Engaging a good
team early in the process and taking a long-term view ensures the best chances of success.
“The involvement and enthusiasm of Woodside personnel right from the start was crucial to the
success of this project. Solving real-world optimisation problems of this nature requires close
collaboration between industry and academic professionals with a diverse range of skills in applied
mathematics, optimisation, computational techniques and modelling, data analysis and operations
engineering.” – A/Prof Ryan Loxton, Department of Mathematics and Statistics, Curtin University