Rapid assessment of infill drilling potential using a simulation-based inversion approach
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It is often difficult to quantify the drilling and recompletion potential in producing gas fields, due to large variability in rock quality, well spacing, well completion practices, and the large number of wells involved. Given the marginal nature of many of these fields, it is often prohibitively expensive to conduct conventional reservoir characterization and simulation studies to determine infill potential. There is a need for rapid, cost-efficient technology to evaluate infill potential in gas reservoirs, particularly tight gas reservoirs. Some authors have used moving window statistical methods, which are useful screening tools for identifying potential areas or groups of wells for further study. But the accuracy of the moving window method in very heterogeneous reservoirs is limited, based on the analysis of some authors. This study presents a new simulation-based inversion approach for rapid assessment of infill well potential. It differs from typical simulation inversion applications in that, instead of focusing on small-scale, high-resolution problems, it focuses on large-scale, coarse-resolution studies consisting of hundreds or, potentially thousands, of wells. In an initial application, the method employs well locations, production data, an approximate reservoir description and, accordingly, is able to identify potential areas or groups of wells for infill development quickly and inexpensively. Prediction accuracy can be increased commensurate with reservoir characterization effort, time and costs. Thus, the method provides a consistent basis for transition from screening studies to conventional reservoir studies.The proposed approach is demonstrated to be more accurate than moving window statistical methods in synthetic cases, with comparable analysis times and costs. In a bind validation study of a field case with 40 years of production history, the method was able to accurately predict performance for a group of 19 infill wells.