Abstract Hydraulic fracturing is crucial for enhancing oil production in tight reservoirs. The success of such projects heavily relies on selecting the right wells and zones for fracturing, which remains a significant challenge during the planning stage due to high uncertainty in performance evaluation. This study aims to improve the accuracy of performance predictions and refine the process of selecting fracturing zones using mathematical optimization techniques to maximize project benefits. Flow rate predictions were enhanced through extensive data acquisition and laboratory tests, including sidewall coring for rock mechanical and proppant permeability tests, dipole sonic logging, borehole imaging, microseismic monitoring, and well testing, enabling systematic calibration of prediction models. Reservoir performance was evaluated using the improved model, and hydraulic fracturing zone selection was optimized with mixed-integer programming to automate the selection process and maximize oil recovery within operational constraints. Insights from data acquisition revealed a more accurate understanding of fracturing geometry, leading to improved determination of critical fracturing parameters. Additionally, results from proppant permeability tests uncovered hidden challenges in hydraulic fracturing operations. The combination of geologically informed learning and data-driven approaches led to model calibration through adjustments in skin factor values, resulting in more accurate flow model predictions. An automated fracturing selection tool was also developed to maximize recovery from candidate wells. The complex optimization engine ensures a consistent optimal hydraulic fracturing strategy. Blind test validation demonstrated significant improvements in prediction accuracy, efficiency, and decision-making speed. This project achieved several significant milestones, including the first successful borehole microseismic monitoring in Southeast Asia, providing unprecedented insights into fracture geometry. Additionally, the implementation of an automated fracturing planning tool leveraged a novel application of integer optimization in the hydraulic fracturing process. This comprehensive hydraulic fracturing framework offers a scalable solution for enhancing operational efficiency and maximizing resource recovery in future hydraulic fracturing operations.
Worapotpisan et al. (Mon,) studied this question.
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