Abstract
Abstract It is excessively crucial for performing production match of parent-child wells automatically by using assisted history matching approach, especially considering the interwell interference scenario. We explored a new EDFM-AI (Embedded Discrete Facture Model-Artificial Intelligence) workflow by taking into account the multiple uncertainty parameters that were composed of fracture height, fracture half-length, fracture conductivity, fracture water saturation, fracture cluster efficiency, and natural fracture conductivity. The history matching algorithm implemented in this study is advanced machine learning model called XGBoost. It could overcome the issue that the proxy model is always imprecise due to the limited training sample data. Specifically, we established a field-scale reservoir model that includes parent-child shale gas wells with fracture hits in the Sichuan basin. By using this sophisticated workflow, two wells’ history matching results are excellent in terms of the inversed solution of the hydraulic fracture properties and natural fracture properties. And then, we perform the production forecasting for parent and child wells, respectively. Consequently, the results show that the best match value for estimated ultimate recovery (EUR) of parent well is 287.4 million cubic meters whereas the best match value for EUR of child well is 187.6 million cubic meters. The reliable reason is that effective fractured area of parent well is larger than that of child well. Besides, the natural fractures have a significant impact on the performance of shale gas wells with fracture hits observed, especially in the short-term production period.
Highlights
Shale gas resources play one of the most significant roles in the global energy revolution and should be regarded as the largest increasing component in global natural gas production
Based on the obtained results, the following conclusions can be drawn from this study: (1) Through this unprecedent work, it is found out that the equivalent fracture flowing area, fracture conductivity, and equivalent cluster efficiency are inferior for the child well
The fracture conductivity and equivalent cluster efficiency for parent well are 156.3 md-m and 47% or 18.4% and 308.7% higher than those of child well (132 md-m and 11.5%). These results imply that natural fracture characterization is crucial to understand the degree of interwell interference, and careful decision makings are required if strong interferences are present
Summary
Shale gas resources play one of the most significant roles in the global energy revolution and should be regarded as the largest increasing component in global natural gas production. Marongiu-Porcu et al [20] built a modified workflow for modeling interwell interference by using unstructured grid method These methods are associated with high computational cost and could not efficiently and accurately simulate complex fracture network coupling with natural fractures. The history matching of well interference between parent and child wells is exponentially difficult, especially considering the differences of hydraulic fracture geometries of parent/child wells, inconformity of cluster efficiency, complexity of natural fracture connection, and so forth. We developed an EDFM-AI workflow for the deep shale gas reservoir model based on field-scale scenario including parent-child well interference and multiple planar hydraulic fractures with abundant natural fractures that forms a complicated network. The production forecasting and model visualization has been studied and generated by our workflow effortlessly
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