Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

Abstract

Accurate estimation of mineral composition is essential for refined reservoir characterization, thermal conductivity and mechanical determinations of sedimentary rocks, but is extremely challenging in shale units due to the mineralogical complexity, low porosity and ultra‐low permeability. Direct mineral measurements derived from laboratory X‐ray diffraction (XRD) analysis on core samples and borehole geochemical logging tool (GLT), and conventional geophysical logs from vertical wells penetrating sediments are widely available in some basins, which enables detailed mineralogical characterization of a well. A hybrid machine learning (ML) architecture that improves model training and validation by combining convolutional neural network (CNN) with XGBoost allows accurate description of the mineralogical compositions across a basin. We applied this ML approach to predict the mineral compositions using conventional well logs from the Horn River Basin, northeast British Columbia, Canada, where extensive drilling for shale‐gas and conventional hydrocarbon resources, complemented by high temperature geothermal energy potential is ideal for case testing. The predicted mineral compositions from the ML approach are consistent with the mineralogical readings from the GLT and are confirmed by the XRD mineral measurements. This allows basin‐wide mineral compositions mapping that reveals spatial trends of major mineral compositions and their relationship with the previously recognized geomechanical and geological features, which have important implications for thermal conductivity modeling, reservoir evaluation and extensive geological studies.

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

“…However, it presents significant difficulties in shale and tight units because of the intricate mineralogical structure, minimal porosity, and extremely low permeability. Lab measurements can be accessed more precisely [53]. The conventional practice used for the assessment of porosity is lab-based measurement of rock samples as well as traditional logging methods, which encounter constraints related to cost, and time.…”

Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

“…In recent years, several investigations have delved into diverse machine learning (ML) methodologies for mineral composition estimation. Hu et al (2023) devised a hybrid ML framework, amalgamating convolutional neural network architecture with XGBoost, while Laalam et al (2022) conducted a comparative analysis of the performance of linear regression (LR), support vector regression, random forest regression (RFR), extra trees regression, K-nearest neighbors, and extreme gradient booster (XGBoost). Conversely, Lee and Lumley (2023) assessed the mineralogical brittleness index of shaly formations employing a blend of statistical and ML techniques, including decision trees, ensembles, support vector machines, probabilistic neural network, and deep feedforward neural network.…”