Optimization of tight gas reservoir fracturing parameters via gradient boosting regression modeling

作者：Huohai Yang, Xuanyu Liu, X. Chu, Binghong Xie, Ge Zhu, Hancheng Li, Jun Yang · 发表于：Heliyon · 年份：2024 · DOI：10.1016/j.heliyon.2024.e27015 · 被引用次数：17 · 研究领域：Hydraulic Fracturing and Reservoir Analysis、Drilling and Well Engineering、Oil and Gas Production Techniques

In China, the exploitation of most unconventional oil and gas reservoirs is dependent on hydraulic fracturing, which is a key method employed when developing tight gas formations. Numerous scholars and field engineers, both domestically and internationally, have conducted extensive numerical simulations and physical experiments to study crack propagation and predict post-fracturing productivity in hydraulic fracturing. Although some progress has been reported in this regard, it is difficult to accurately predict the well productivity using mechanistic models owing to the vertical multilayered development of tight gas reservoirs. In this study, vertical fractured wells in a block of Sulige gas field were examined. The block relied on hydraulic fracturing to produce tight gases. However, as development progressed, the available reservoir environment deteriorated, large differences emerged between wells after fracturing, and the fracturing results did not meet the expectations. In this study, geological, construction, and generation data for this block that had been collected since 2007 were analyzed. After applying multiple machine-learning methods to filter outliers and fill in missing values, k-means clustering, classification enhancement, extreme gradient enhancement, and LightGBM algorithms were used to establish a regression model. The analysis results revealed that the regression accuracy of the cluster test set was as high as 70% and that the LightGBM model had the best ...