Reservoir Fracturability Evaluation for Tight Formations Based on Markov Chain Monte Carlo Risk Theory

Summary Reservoir fracturability is a crucial indicator for assessing the feasibility of hydraulic fracturing stimulation. However, existing techniques for evaluating tight sandstone reservoir fracturability have limitations and may not provide comprehensive assessments. Geological, reservoir, and mechanical parameters affect the fracturability of tight sandstone reservoirs. Moreover, measurement errors, geological heterogeneity, and data loss directly affect the reliability and robustness of fracturability evaluation. In this study, we propose a method for evaluating the confidence of the fracturability index. The method considers both geological and engineering “dual sweet spots” using Markov Chain Monte Carlo (MCMC) risk theory. Initially, the method integrates neighboring well data and considers the potential high correlation among multiple parameters. We use the Gibbs sampling method, based on MCMC simulation, to obtain the multidimensional joint distribution of the parameters and analyze their uncertainty. Subsequently, we establish a fracturability index calculation model using a comprehensive approach that involves subjective and objective weighting methods. This model incorporates parameter distributions to obtain the probability distribution of the fracturability index. Finally, we applied this method to the entire well section and yielded a fracturability profile with confidence, rather than a fixed profile, which enables informed reservoir delineation and fracturing design decisions. Field application in a representative well and its neighboring well at the Tarim Basin, China, demonstrated the effectiveness and adaptability of the proposed method in guiding differentiated fracturing design strategies based on fracability confidence, resulting in improved hydraulic fracturing effectiveness and production performance.