Quantitative Characterization and Risk Classification of Frac Hit in Deep Shale Gas Wells: A Machine Learning Approach Integrating Geological and Engineering Factors

Bo Zeng, Yuliang Su, Jianfa Wu, Dengji Tang, Ke Chen, Yi Song, Chen Shen, Yongzhi Huang, Yurou Du, Wenfeng Yu

With the continued advancement of shale gas development, the issue of frac hit has become increasingly prominent and has emerged as a key factor influencing the production of shale gas wells. Quantitative evaluation of the impact of frac hit on shale gas wells and proposing different methods to prevent frac hit are of great significance for the efficient development of shale gas. This research puts forward a machine learning-based workflow that incorporates geological and engineering factors to evaluate the impacts of frac hit. The “Frac Hit Pressure Integral Index (FPI)” quantifies the dynamic pressure responses by means of the ratios of initial pressure to shut-in pressure. Pearson analysis is employed to reduce the dimensionality of parameters, and Random Forest and K-means++ algorithms are utilized to classify the risks of frac hit. Among numerous influencing factors, it has been found that the brittleness index and well spacing possess the highest weights among the geological and engineering influencing factors, reaching 20.4 and 16.1, respectively. The L well area of southern Sichuan shale gas lies in the Fuji syncline of the Huaying Mountain tectonic system’s low-fold Fujian zone. When applied to the L well area in the Sichuan Basin, the results pinpoint the brittleness index, fluid intensity, and well spacing as crucial factors. It is recommended that, for reservoirs with high fracturability, reducing fluid intensity and increasing well spacing can minimize inter-well interference. This workflow classifies risks into low (FPI ≤ 265.43), medium (265.43 < FPI < 658.56), and high levels (FPI ≥ 658.56) and recalibrates natural fracture zones based on pressure and flowback data, thereby enhancing the alignment between geological and engineering aspects by 10%. This framework optimizes fracturing designs and mitigates inter-well interference, providing support for the efficient development of shale gas.