Benchmarking of Restimulation Candidate Selection Techniques in Layered, Tight Gas Sand Formations Using Reservoir Simulation

Abstract

Abstract Studies by the Gas Research Institute have revealed that improved methods are needed to cost-effectively identify high-potential restimulation candidate wells. Subsequent research has had the objective of developing such methodologies, and testing them in the field. The techniques being investigated include production statistics, virtual intelligence, and type-curves. For various reasons, field activities have been slow to implement, limiting the feedback needed to fully test each candidate selection method. Therefore a reservoir simulation study was performed to test the methods. The simulation field model consisted of four reservoir layers of variable properties. Wells were drilled in three rounds over a 12-year period (120 total wells). Completion intervals were varied for each well, as were skin factors for individual layers. Before providing the data to the project team for analysis, noise was added. These model features and noise were incorporated into the exercise to best replicate actual field conditions. Restimulation potential was established by "restimulating" each well in the model and observing the incremental production response. Application of the various candidate selection techniques, and comparing the results to the known answer, has yielded several important conclusions. First, simple production data comparisons are not effective at identifying high-potential restimulation candidates; better producing wells tend to be better restimulation candidates. Virtual intelligence techniques were the most successful, correctly identifying over 80% of the theoretical maximum available potential. The type-curve technique was not as effective as virtual intelligence, but still achieved a 75% candidate selection efficiency.