Understanding Gas Production Mechanism and Effectiveness of Well Stimulation in the Haynesville Shale Through Reservoir Simulation

Abstract

Abstract A unique workflow and methodology enabled analysis of production data using reservoir simulation to help understand the shale gas production mechanism and the effectiveness of stimulation treatments along the lateral of horizontal wells. Starting from early 2008, we have analyzed production data from more than 30 horizontal wells in the Haynesville Shale using this methodology. This paper presents case studies demonstrating results of this new technique in several different areas of the Haynesville Shale. After integration of all available data, we built simulation models for the wells stimulated with multistage hydraulic fracture treatments. This modeling work investigates factors and parameters relating to short- and long-term well performance including 1) pore pressure, 2) matrix rock quality, 3) natural fractures, 4) hydraulic fractures, and 5) complex fracture networks. By historymatching the observed production, we have identified the primary factors for creating good early well performance. The Haynesville study has provided a better understanding of the gas production mechanism and effectiveness of stimulation along the laterals. After calibration of the simulation model, effective well drainage area and reserve potential can be calculated with more confidence. The Haynesville Shale is a very tight source rock. The shale matrix quality correlates with production performance when stimulation treatments are consistent along the lateral. A complex fracture network created during the stimulation treatment is the key to generating superior early well performance in the Haynesville Shale. Knowing how to effectively create more surface area during treatment and preserve the surface area after treatment are critical factors for making better wells in the Haynesville. Operators can use this information to determine where and how to spend resources to produce better wells. It also helps refine expectations for well performance and minimizes the uncertainties of developing these properties. The workflow and methodology have also been successful in other shale plays.