From Streamlines to Fast Marching: Rapid Simulation and Performance Assessment of Shale-Gas Reservoirs by Use of Diffusive Time of Flight as a Spatial Coordinate
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SummaryCurrent industry practice for characterization and assessment of unconventional reservoirs mostly uses empirical decline-curve analysis or analytic rate- and pressure-transient analysis. High-resolution numerical simulation with local perpendicular bisector (PEBI) grids and global corner-point grids has also been used to examine complex nonplanar fracture geometry, interaction between hydraulic and natural fractures, and implications for the well performance. Although the analytic tools require many simplified assumptions, numerical-simulation techniques are computationally expensive and do not provide the more-geometric understanding derived from the depth-of-investigation (DOI) and drainage-volume calculations.We propose a novel approach for rapid field-scale performance assessment of shale-gas reservoirs. Our proposed approach is dependent on a high-frequency asymptotic solution of the diffusivity equation in heterogeneous reservoirs and serves as a bridge between simplified analytical tools and complex numerical simulation. The high-frequency solution leads to the Eikonal equation (Paris and Hurd 1969), which is solved for a diffusive time of flight (DTOF) that governs the propagation of the pressure front in the reservoir. The Eikonal equation can be solved by use of the fast-marching method (FMM) to determine the DTOF, which generalizes the concept of DOI to heterogeneous and fractured reservoirs. It provides an efficient means to calculate drainage volume, pressure depletion, and well performance and can be significantly faster than conventional numerical simulation. More importantly, in a manner analogous to streamline simulation, the DTOF can also be used as a spatial coordinate to reduce the 3D diffusivity equation to a 1D equation, leading to a comprehensive simulator for rapid performance prediction of shale-gas reservoirs. The speed and versatility of our proposed method makes it ideally suited for high-resolution reservoir characterization through integration of static and dynamic data.The major advantages of our proposed approach are its simplicity, intuitive appeal, and computational efficiency. We demonstrate the power and utility of our method by use of a field example that involves history matching, uncertainty analysis, and performance assessment of a shale-gas reservoir in east Texas. A sensitivity study is first performed to systematically identify the heavy hitters affecting the well performance. This is followed by history matching and an uncertainty analysis to identify the fracture parameters and the stimulated-reservoir volume. A comparison of model predictions with the actual well performance shows that our approach is able to reliably predict the pressure depletion and rate decline.
