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Artificial Neural Network Modeling and Forecasting of Oil Reservoir Performance

  • Ehsan AmirianEmail author
  • Eugene Fedutenko
  • Chaodong Yang
  • Zhangxin Chen
  • Long Nghiem
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

A detailed uncertainty analysis on numerical flow simulation models preserving a robust and reliable model for an oil and gas reservoir is often deterministic, cumbersome, and expensive (manpower and time-consuming). Presence of a high-dimensional data space consisting of a large number of operational and geological parameters impedes practical decision-making and future performance prediction for oil and gas recovery processes. Thus, the rise of uncertainty-based reservoir development scenarios has provoked reservoir engineers to look for alternative modeling techniques that are capable of being reevaluated numerous times to examine the impact of specific variables or probing a range of scenarios on production profiles. This study reviews simulation data-driven proxy models that can be used instead of an actual reservoir simulator by using data interpolation in a high-dimensional parameter space.

The current study integrates a systematic data analysis and numerical flow simulations to create a comprehensive data set for different recovery processes, which entails different characteristics labeling reservoir heterogeneities and other pertinent operational constraints. This representative data set is then utilized as the building blocks for a cognitive data-driven proxy modeling workflow. Artificial and computational intelligence techniques are used to cognitively train a data-driven proxy model. The trained proxy will be subsequently employed to predict the production performance for the underlying process in presence of new reservoir development scenarios. The predictability of the designed cognitive proxy model is evaluated via comparing the results from proxy with those from a commercial simulator. Particularly, we compare the performance of two types of interpolation models: the radial basis function neural network (RBF NN) and the multilayer Levenberg-Marquardt neural network (ML LM NN). Results of studies of nine different cases of SAGD, black oil, and unconventional reservoirs will be presented to illustrate the current approach.

The presented results and performance characteristics associated with data-driven proxy models which can be reevaluated much faster than explicit models for the underlying process highlight the great potential of this modeling approach to be integrated directly into most existing reservoir management routines. This paper provides a viable tool to overcome challenges related to dynamic assessment of uncertainties during history matching of recovery processes and signifies the ability of cognitive proxy modeling in future performance prediction of oil and gas recovery processes.

Keywords

SAGD Data-driven Proxy modeling Numerical flow simulation Artificial and computational intelligence 

Abbreviations

ACI

Artificial and computational intelligence

ANN

Artificial neural network

BO

Black oil

GlobalHmError

Global history matching error (%)

HL

Hidden layer

HM

History matching

LHD

Latin hypercube design

LM

Levenberg-Marquardt

ML

Multilayer

NN

Neural network

NPV

Net present value

OF

Objective function

OL

Output layer

RBF

Radial basis function

SL

Single layer

SAGD

Steam-assisted gravity drainage

UA

Uncertainty analysis

Notes

Acknowledgments

The authors would like to thank Computer Modeling Group Ltd. for permission to publish this paper. This research is supported by the NSERC/AIEES/Foundation CMG and AITF (iCORE) Chairs.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ehsan Amirian
    • 1
    Email author
  • Eugene Fedutenko
    • 2
  • Chaodong Yang
    • 2
  • Zhangxin Chen
    • 1
  • Long Nghiem
    • 2
  1. 1.Department of Chemical and Petroleum Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Computer Modelling Group Ltd.CalgaryCanada

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