Development and testing of an expert system for coalbed methane reservoirs using artificial neural networks

Reservoir simulators are effectively used in predicting the production performances of oil and gas reservoirs within a good level of accuracy. However, during initial stages of exploitation, our knowledge on most of the reservoir properties and production parameters involve a good degree of uncertainty. In such cases, expert systems can be effective as a screening tool by mimicking the performance of a reservoir simulator at a lower cost and reduced personnel and machine time. The expert system described in this paper is a tool which can be used to predict the performance of a coalbed methane reservoir similar to its numerical counterparts. The proposed expert system has been trained with the help of an extensive data base and has the capability of providing gas and water production profiles for a period of about ten years for a given coalbed methane reservoir. Other outputs include cumulative gas and water production, peak gas flow rate expected, time to achieve a peak rate and abandonment time. This study also involves the development of an inverse expert system which has the capability of identifying the optimum production strategies for a desired production scenario from a coalbed reservoir. Conventional reservoir simulators cannot suggest an optimized design strategy that can help achieve a certain desired production performance from a coal seam unless a large number of scenarios are studied. The inverse model can tackle this optimization problem quite effectively.