An integrated assisted history matching and embedded discrete fracture model workflow for well spacing optimization in shale gas reservoirs

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2020-12

Authors

Li, Qiwei, M.S. in Engineering

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Abstract

An appropriate well spacing plan is critical for the development of shale reservoirs. The biggest challenge for well spacing optimization is interpreting the subsurface uncertainties associated with hydraulic and natural fractures. However, most studies have calibrated the uncertainties by single history matching, which does not take the non-uniqueness of history matching into account. Therefore, the objective of this study is to develop an integrated assisted history matching (AHM) and embedded discrete fracture model (EDFM) workflow for well spacing optimization by considering multiple uncertainty realizations and economic analysis. We applied this workflow for an actual shale gas reservoir without natural fractures and another shale gas reservoir with complex natural fractures. Firstly, we captured the distribution of uncertainty parameters of matrix and fractures using AHM based on the production data. Uncertain parameters of matrix include matrix permeability, matrix porosity, and three relative permeability parameters, while hydraulic fractures uncertainties consist of fracture height, half-length, width, conductivity, and water saturation. And the uncertain parameters of natural fractures are the number of natural fractures, conductivity, and length. The input cases can be prepared by combining AHM solutions with different well placement scenarios. Then we performed reservoir simulation to all cases and forecasted the gas and water production in the long-term. Gas estimated ultimate recovery (EUR) per well can be analyzed to predict the influence of well interference and the critical well spacing. Finally, we estimated the net present value (NPV) for all cases and predicted it by k-nearest neighbors (KNN) proxy model to better understand the relationship between well spacing and NPV. The optimum well spacing can be obtained from the maximum NPV. Our integrated workflow is straightforward and practical, with great accuracy, and efficiency. We can predict the optimum well spacing for most shale gas reservoirs by capturing the multiple realizations of uncertainties

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