Benchmarking of Restimulation Candidate Selection Techniques in Layered, Tight Gas Sand Formations Using Reservoir Simulation
- S.R. Reeves (Advanced Resources Intl.) | P.A. Bastian (Schlumberger Holditch-Reservoir Technologies) | J.P. Spivey (Schlumberger Holditch-Reservoir Technologies) | R.W. Flumerfelt (Schlumberger Holditch-Reservoir Technologies) | S. Mohaghegh (West Virginia University) | G.J. Koperna (Advanced Resources Intl.)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 1-4 October, Dallas, Texas
- Publication Date
- Document Type
- Conference Paper
- 2000. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.8.1 Tight Gas, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 4.6 Natural Gas, 5.5 Reservoir Simulation, 2.4.3 Sand/Solids Control, 4.1.5 Processing Equipment, 1.6 Drilling Operations, 7.6.6 Artificial Intelligence, 5.4.2 Gas Injection Methods
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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.
The Gas Research Institute began investigating the potential of restimulating existing natural gas wells as a source of incremental, low-cost reserves in 1996. Initial studies revealed that the potential was substantial (particularly in tight sand formations), but improved methods were required to cost-effectively and reliably identify high-potential restimulation candidate wells.1 This need was underscored by an observation that 85% of the restimulation potential for a given field appears to exist in only 15% of the wells; identification of that 15% is therefore critical to restimulation economics, but comprehensive field studies specifically for this purpose are too costly to justify.
Based on these findings, subsequent research initiated in 1998 has had the objective of developing a cost-effective and reliable restimulation candidate selection methodology, and testing it in the field.2 The techniques being investigated for the methodology include production statistics,3 virtual intelligence4,5,6,7 and engineering-based type-curves.8,9 The techniques were to be applied to four field test sites in the Green River, Piceance, East Texas and South Texas basins, each consisting of 200-300 wells and at which five restimulation treatments were to be performed.10,11 For various reasons, field activities have been slow to implement, limiting the feedback needed to fully examine the effectiveness of and optimize each candidate selection technique. In order to advance methodology development in a timely manner, a different approach was needed to validate the performance of each individual candidate selection technique.
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