Propagating Image-Based Rock Classes From Cored Wells to Non-Cored Wells Using Supervised Machine Learning for Enhanced Formation Evaluation
Authors:   The University of Texas at Austin, Sahu Pallavi , Gonzalez Andres ,   The University of Texas at Austin, Heidari Zoya ,   The University of Texas at Austin, Lopez Olivier ,   Equinor
Published: November 2024

High-resolution image data are instrumental in quantifying the rapid variation of rock fabric in formation evaluation that conventional well logs fail to capture. However, the acquisition of high-resolution image data for all wells of an entire formation is expensive and time consuming. Therefore, extrapolation of rock fabric information of expert-derived facies from high-resolution core image data to non-cored wells is necessary for efficient formation evaluation of non-cored wells. The objectives of this paper include (a) to train supervised learning models in wells with core images or image logs to identify image-based rock classes in un-imaged wells using conventional well logs, (b) to compare the performance and accuracy of the obtained supervised learning models to classify the lithofacies using only conventional logs and estimated petrophysical and compositional properties of nearby wells without any image data, and (c) to derive class-based petrophysical models to enhance formation evaluation using conventional well logs in wells without image data. First, we conduct conventional formation evaluation to estimate petrophysical properties such as porosity, permeability, water saturation, and volumetric concentrations of minerals. Then, we employ conventional well logs and estimated petrophysical properties with a feature ranking method for feature selection prior to model training. The selected predictor features and image-based rock classes are used as inputs to train the selected models. We train three supervised learning models—random forest, gradient boosting, and support vector machines—to predict image-based rock classes in nearby wells without image data. We used a k-fold cross-validation approach to train the models and tune the hyperparameters. Finally, we used class-based petrophysical models from the cored well to estimate class-based petrophysical properties in the nearby wells. We apply the proposed workflow to two wells in siliciclastic intervals with rapid spatial variation in rock fabric. First, the cored well is used to train/test supervised learning models, honoring high-resolution CT-scan images and core photos incorporating rock fabric. The trained models are used in a nearby well to predict image-based rock classes. Comparison of predicted image-based rock classes in the nearby well using the trained models with expert-derived lithofacies resulted in accuracies of up to 80%. The time spent in the training step for the support vector machines algorithm was significantly lower than for the cases of the other two algorithms, up to four times faster than the random forest algorithm and five times faster than the gradient boosting algorithm, with similar accuracy values. Finally, class-based estimates of petrophysical properties in the nearby well decreased the mean relative error by 20% compared to formation-based estimates. The results of the proposed workflow will contribute to extrapolating high-resolution core rock fabric information acquired in cored wells to nearby un-imaged wells. The novelties of this workflow include (a) incorporating rock fabric information that improves accuracy in predicting the petrophysical properties and rock types of nearby un-imaged wells, (b) speeding up and improving the process of class-based formation evaluation with minimal calibration efforts, and (c) optimizing the need for imaging (core images or image logs) efforts.