Quantitative Analysis of Rate Transient Analysis in Unconventional Shale Gas Reserviors

Rate Transient Analysis is a quick reservoir modeling solution that has been used throughout the oil and gas industry over its continuous development and has provided breakthroughs for modeling conventional plays for decades. As the Marcellus Shale play continues to be a massive producer of Natural Gas in the world, operators look to find economical yet fairly accurate solutions to develop accurate reservoir models of their wells given the complex nature of unconventional reservoirs. Due to extremely low permeability and heterogeneity along with its complex fracture networks, it becomes an extremely difficult problem to model and predict the fluid flow behavior of producing wells. Currently, many operators across the region generate RTA (Rate Transient Analysis) models to forecast production and track well performance along the lifespan of the well. With RTA being a solution that was well applied to conventional oil wells with success, the solution was modified to fit the uncertain nature of unconventional shale plays such as the Marcellus Shale. When doing such, the problem of modeling complex fracture networks generated during hydraulic fracturing of the well is simplified along with adding certain assumptions of parameters are used to generate a model. This process paired with the use of history matching which generates numerous cases altering the unknown parameters until the wells historical production data and simulated production is matched, deeming a “correct model.” The following study analyzes in depth the use of RTA in unconventional shale reservoirs and the current methodology of combining assumptions with history matching in order to develop reservoir “model matches.” In doing so, the workflow provided by IHS Harmony, a popular RTA model software, will be used to demonstrate user bias and the variance in model results on repeated wells. The workflow consists of using the Blasingame Fracture Typecurve, Agarwal Fracture Typecurve, and Wattenbarger Typecurve as a guide into the analytical models. Once these analyses are completed, the analytical models URM (Unconventional Reservoir Module) Superposition Time and FMB (Flowing Material Balance) are used to generate a drainage area and matrix permeability for the numerical model. Finally, the Multiphase Numerical Model tool will be used in order to develop a model match after iterating on fracture half-length and dimensionless fracture conductivity. The following study will work to highlight user bias in completing these models through several wells. In doing such models of the same accuracy in regard to production history were found after using vastly different reservoir engineering assumptions such as fracture efficiency