Automatic Estimation of Fracture Properties in Multistage Fractured Shale Gas Horizontal Wells for Reservoir Modeling

Owing to low productivity of vertical wells in shale gas formations, operators have started to drill horizontal wells in these ultra-tight gas formations. Wells that are drilled in shale formations have undergone massive hydraulic fracturing treatments to improve the productivity. Since the introduction of horizontal drilling in shale formations, wells have been hydraulically fractured at several stages. Each stage is located a few hundred feet away from the other stages. Multi-Stage fracturing of horizontal drilling in shale gas wells increases the production significantly. Integration of detailed fracture network mapping in numerical simulation of these reservoirs is impractical because of high resolution time-space seismic data required to map the fracture surfaces, which in most wells are not acquired because of economic considerations. Multi-million cell numerical models are required for each individual well due to massive refining of gridblocks around the fractures. Conditioning the refined reservoir models to performance data adds to the complexity of the problem, since any adjustments made to the fracture properties can change the gridding of the model. Hence, automatic history matching tools are not well suited for application in grid-refined, detailed geological models representing shale gas reservoirs and wells. In this study, we propose a new multi-stage compartmentalized numerical modeling technique for shale gas reservoirs, which is an extension to dual porosity modeling. The methodology is simplistic, is not built using detailed high resolution fracture maps, and requires less CPU time. Ensemble Kalman Filter (EnKF), a minimum mean square error estimation tool, is used for estimating the reservoir properties. Combined EnKF/compartmentalized modeling technique is implemented in this paper to automatically estimate the fracture properties of the fracturing stages of a horizontal well. The algorithm only requires well’s performance data, such as gas production rates, flow back volumes, bottom-hole pressures, and tracer concentrations. Numerical models that are conditioned to observations are shown to accurately estimate the production rates and are useful for reserves booking and field development. Introduction Shale gas resources are abundant in the U.S. and across the world. Between early 1800s and 2000, more than 20,000 wells have been drilled in the shale formations of United States (Hill and Nelson, 2000). U.S. shale gas production has increased sevenfold between 1979 and 1999 (Curtis, 2002). By 2007, 225 to 248 TCF of gas was estimated to be stored in Devonian and Ohio shale reservoirs of the Appalachian basin in Eastern United States (Matthews et al., 2007). According to U.S. Energy Information Administration (EIA), 6622 TCF of gas is estimated to be technically recoverable from world’s shale gas resources while U.S. accounts for approximately 29% of that amount (Newell, 2011). By January 2009, Marcellus shale in Eastern United States was estimated to contain 410 TCF of gas (Independent Statistics and Analysis, 2011). Marcellus’s gas in place is now evaluated to be between 295 and 2700 TCF (Lee et al., 2011). Economic production from these ultra-tight formations became possible in the past few decades by advancements in technologies such as horizontal drilling and hydraulic fracturing. By 2000, approximately 4 TCF of gas was produced from the U.S. shale gas reservoirs, proved reserves were 4 TCF, technologically recoverable gas was estimated to be between 31 and 76 TCF, and undiscovered gas accounted for another 131.3 TCF (Hill and Nelson, 2000). By 2006, the shale gas proved reserves increased to 92 TCF, from which 58 TCF of gas had been produced, technologically recoverable gas was 185 TCF, undiscovered gas estimates jumped to 350 TCF, and Gas Technology Institute (GTI) estimated that 5000 TCF of gas is stored in all the U.S. tight basins (Holditch, 2006).