Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

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.