Interpretable fracturing optimization of shale oil reservoir production based on causal inference

Abstract

The micro- and nanopore throats in shale oil reservoirs are finer than those in conventional oil reservoirs and have a larger specific surface area, potentially resulting in a more pronounced crude oil boundary effect. The prediction of recoverable reserves in shale oil reservoirs is influenced by factors such as geological complexity, fracture characteristics, and multiphase flow characteristics. The application of conventional reservoir seepage theories and engineering methods is challenging because of the unique characteristics of shale formations. A novel computational framework is proposed for the prediction of recoverable reserves and optimization of fracturing parameters by combining machine learning algorithms with causal discovery. Based on the theory of causal inference, the framework discovers the underlying causal relationships of the data, mines the internal laws of the data, and evaluates the causal effects, aiming to build an interpretable machine learning model to better understand the properties of shale oil reservoirs. Compared to traditional methods, the interpretable machine learning model has an outstanding prediction ability, with R² of 0.94 and average error as low as 8.57%, which is 5.22% lower than that of traditional methods. Moreover, the maximum prediction error is only 21.84%, which is 25.2% smaller than the maximum error of traditional methods. The prediction robustness is good. An accurate prediction of recoverable reserves can be achieved. Furthermore, by integrating particle swarm optimization and TabNet, a fracturing parameter optimization model for shale oil reservoirs is developed. According to an on-site validation, this optimization results in an average increase of 13.45% in recoverable reserves. This study provides an accurate reference for reserve assessment and production design in the exploration and development of shale oil reservoirs.

Graphical Abstract