A Gaussian Process Regression Model for Estimating Pore Volume in the Longmaxi Shale Formation

Shale pore volume is a critical parameter for reservoir evaluation. Accurate and rapid determination of this parameter is essential for identifying sweet spots and performing reliable reserve estimations. Currently, laboratory experiments remain the standard for determining pore volume; however, these methods are typically time-consuming, costly, and labor-intensive. To complement traditional experimental approaches, we developed a Gaussian Process Regression (GPR) model to estimate shale pore volume based on mineralogical compositions. The model is specifically tailored for the Longmaxi shale, utilizing six input features: the contents of Total Organic Carbon (TOC), clay, quartz, feldspar, carbonate, and pyrite. The GPR model achieved a mean absolute percentage error (MAPE) of 9.97% on the testing dataset, while it yielded an MAPE of 17.66% when applied to an additional independent validation set. Finally, a sensitivity analysis using the Shapley additive explanations was conducted to elucidate the influence of mineralogical constituents on shale pore volume.