Development and Testing of an Expert System for Coalbed Methane Reservoirs using Artificial Neural Networks

Open Access
Author:
SRINIVASAN, KARTHIK
Graduate Program:
Petroleum and Natural Gas Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 18, 2008
Committee Members:
  • Dr Luis Ayala, Thesis Advisor
  • Turgay Ertekin, Thesis Advisor
  • Zuleima T Karpyn, Thesis Advisor
Keywords:
  • Expert Systems
  • Coalbed Methane Reservoirs
  • Artificial Intelligence in Reservoir Engineering
  • Artificial Neural Networks
Abstract:
Reservoir simulators serve as excellent tools in predicting production performance of oil and gas reservoirs at a good level of accuracy. However, during initial stages of exploitation, for most of the reservoir properties and production parameters, there is a good level of uncertainty. In such cases, Expert Systems offer a screening tool to achieve the purpose of a simulator at a lower cost and reduced time. An expert system consists of a knowledge base and set of algorithms that are capable of inferring facts based on existing knowledge and incoming data. The level of accuracy of predictions from an expert system depends on the quality of data, rules that define the problem at hand and human expertise. Artificial neural networks are being used in a large number of reservoir engineering applications such as performance optimization, reservoir characterization, field development applications, well stimulation, formation evaluation and pressure transient analysis. The expert system described in this work is a tool which can be used to predict the performance of a coalbed methane reservoir just like any other numerical model. Starting with a simple model where predictions are confined to coal seams of known reservoir parameters and varying production parameters, a generalized model is developed. The developed expert system is designed for universal applications and provides gas and water production profiles for a period of about ten years as outputs. In addition to this model, an inverse expert system, in which, for an expected percentage recovery from a coal seam, optimum ranges of well design parameters that satisfy the requirements of the producer are predicted. Conventional reservoir simulators may not provide the user with a list of suggestive design parameters that can help achieve a certain desired production performance from a coal seam of known reservoir properties before it can be put into production. This is achieved by the inverse model which otherwise can only be accomplished by trial and error procedures. Understanding the ability of the network to predict the output parameters is crucial in such developments. As expected, the complexity of the network topology is bound to increase with increased number of inputs and outputs. Due to inherently existing nonlinearities in relationships between inputs and outputs, several modifications are required to institute a sound combination of inputs that can improve predictions significantly. With an orderly and improvised procedure, a robust expert system that can be used in optimizing the coal bed methane production applications is developed and tested successfully.