The paper represents the way the conventional method of candidate well selection can be optimized with the fuzzy sets theory applied.

At late stages of any oil field life the most efficient way to support high hydrocarbon production level tends to be well stimulation procedures carried on the continuing basis. Considering any well stimulation treatment prospective the main thing is the issue of oil remaining recoverable reserves in-place.

At present time there are two methods for well stimulation treatment candidate selection used in Tomskneft JSC. The first method is reservoir simulation modelling (RSM), this allows determination of the areas where appreciable amount of recoverable reserves is located and which well is better to retrieve them. However, the crucial disadvantage of this approach is its dependence on how good the model is adopted. The calculations made by reservoir simulation are rarely confirmed when a real well is run.

Keywords:
production control, downhole intervention, production monitoring, fuzzy logic, Reservoir Characterization, Artificial Intelligence, production enhancement, well stimulation treatment prospective, selection, well candidate recognition
Subjects:
Well & Reservoir Surveillance and Monitoring, Reservoir Characterization, Reservoir Simulation, Information Management and Systems, Well candidate recognition, Artificial intelligence, Well Operations and Optimization, Well Intervention, Well Operations, Optimization and Stimulation
Copyright 2015, Society of Petroleum Engineers
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