QUT-MIT Mining and Energy Symposium
Spatial data in unconventional oil and gas –
The hidden information in location
Justin B. Montgomery
PhD Researcher, MIT
May 26, 2016
Challenge: Choosing where to drill a well or acquire a lease in an oil field
based on productivity of existing wells
Case study: Identify best remaining drilling locations in Bakken oil field
3497 wells
50 km
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Tobler’s First Law of Geography: “Everything is related to everything else,
but near things are more related than distant things”
– In shale gas and tight oil fields, there are spatial trends in geological conditions
– Location can serve as a proxy for unknown subsurface parameters influencing productivity by using
statistical similarity of outcomes in wells close to each other to infer this “hidden” information
– Data from wells is inherently spatial
 To get the maximum information out of data from existing wells, we should model the spatial
relationships between these wells
To demonstrate the “hidden” information in location, we will use 3 predictive
linear models with increasing spatial resolution
Model 1: Technical parameters without location
 Horizontal length
of completed well
 Amount of water
injected to create
fissures
 Amount of sand
(proppant) injected to
“prop” fissures open
Model 2: Add macro spatial
trend
 Surface trend fit of productivity
to coordinates
Model 3: Add micro spatial trends
 Residuals from surface trend
fit are spatially correlated with
each other
 Inverse distance weight this
information
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By accounting for macro and micro spatial trends in productivity, we improve
our prediction quality
Model 2: Technical
parameters + macro
trends
Model 1: Technical
parameters, no location
Model 3: Technical
parameters + macro
trends + micro trends
Further applications and extensions
– Evaluate companies
– Measure impact of
technology and optimize
– Improve resource forecasts
– Approach can be extended
to a nonparametric model