Kamy Sepehrnoori is a professor in the Department of Petroleum and Geosystems Engineering at The University of Texas at Austin, where he holds the W. A. (Monty) Moncrief Centennial Chair in Petroleum Engineering. His research interest and teaching include computational methods, reservoir simulation, parallel computing, enhanced oil recovery modeling, naturally fractured reservoirs, modeling of asphaltene deposition in reservoirs and wellbores, and unconventional reservoir simulation. He is the director of the Reservoir Simulation Joint Industry Project and the Deputy Director of the Center for Petroleum and Geosystems Engineering. He holds a PhD degree from The University of Texas at Austin.

 

The economic production of gas and oil from shale reservoirs requires effective stimulation of low-permeability rock through multi-stage hydraulic fracturing in combination with horizontal wells. The process of hydraulic fracturing often creates complex fracture networks, especially in some shale reservoirs with a high brittleness and a large amount of pre-existing natural fractures. However, accurate modeling and simulation of fluid flow from the complex fracture networks is challenging. Hence, we developed an embedded discrete fractuer model (EDFM) for several in-house reservoir simulators, which can handle the complexity and heterogeneity of a typical fractured shale reservoir. Gas desorption effect and geomechanics, i.e., stress-dependent fracture conductivity, are included. Also, we developed an efficient semi-analytical approach to simulate fluid production from shale reservoirs with the complex fracture networks. In addition, there are high cost and uncertainty due to many uncertain parameters. Therefore, quantifying uncertainty and optimization of fracturing design in an efficient and practical way is clearly desirable. For this challenging issue, we present an integratd simulation platform for unconventional reservoirs (ISPUR) by integrating numerical simulation, analytical solution, economic analysis, Design of Experiment (DOE) and Response Surface Methodology (RSM). Specifically, we first use DOE to perform a sensitivity study with purpose of quantifying rank of important factors and screening insignificant ones. Next, we use RSM to perform optimization to obtain the optimal fracturing design and to maximize net present value (NPV). This framework is effective and efficient for hydraulic fracturing design and production scheme optimization in shale reservoirs.