Spatial variability of petrofacies using supervised machine learning and geostatistical modeling: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA

Abstract

The Sycamore Formation at Sho-Vel-Tum Field primarily consists of clay-rich mudstones and quartz-rich siltstones. The clay-rich mudstones are mainly composed of clays, quartz grains, some allochems and detrital organic matter. The siltstones are structureless and are divided into two petrofacies: high porosity and permeability massive calcareous siltstones (MCSt) and low porosity and permeability massive calcite-cemented siltstones (MCcSt). Core and well-log data provide mineralogical, lithological, and porosity information that is useful to define petrophysical facies (petrofacies) and to create facies logs within the Sycamore Formation. We used the data to establish the Sycamore Formation stratigraphic architecture and to map its spatial variability and reservoir properties. To classify the Sycamore Formation petrofacies in non-cored wells we developed a machine learning-based workflow that compares over 1,800 classification models and selects the best combination of well logs, algorithms, and hyperparameters to predict defined petrofacies. The process includes combinations of well logs that were optimized in four classification algorithms: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). To adjust each classifier, we used a Grid-Search and a 5-fold cross-validation to find the best combination of three hyperparameters to improve results of each algorithm. This workflow allows for the efficient extraction of information from cores at a low cost. After we generated petrofacies logs in non-cored wells, we combined them with multiple constraints to create a 3D petrofacies model for the Sycamore Formation at Sho-Vel-Tum Field and analyze the stratigraphic and diagenetic controls on petrofacies and its impact in reservoir quality.