Estimating Fluid Saturation Using Machine Learning: A Case Study of Niger Delta Sandstone Reservoirs

Abstract Water Saturation (Sw) is a critical input to reserves estimation and reservoir modeling workflows which ultimately informs effective reservoir management and decision-making. Without laboratory analysis on expensive core data, Sw is estimated using traditional correlations—commonly Archie's equation. However, using such a correlation in routine petrophysical analysis for estimating reservoir properties on a case-by-case basis is challenging and time-consuming. This study employs a data-driven approach to model Sw in Niger Delta sandstone reservoirs using readily available geophysical well logs. We evaluate the performance of several generic and ensemble machine learning (ML) algorithms for predicting Archie's computed Sw. ML techniques such as unsupervised anomaly detection and multivariate single imputation were used for preprocessing the data and feature engineering was used to improve the predictive quality of the input well logs. The generalization ability of the ML models was assessed on the individual training wells as well as a held-out test well. Model hyperparameters were tuned using Bayesian Optimization in the cross-validation process to achieve a high rate of success. Several evaluation metrics and graphical methods such as learning curves, convergence plots, and partial dependence plots (PDPs) were then used to assess the predictive performance of the models and explain their behavior. This revealed the Tree Boosting ensembles as the top performers. Moreover, the superior performance of the Tree Boosting ensembles over the benchmark linear model reveals that the relationship between the transformed logs and Sw is complex and better modeled in the nonlinear domain. Based on the results obtained in this research, we propose the Tree Boosting ensembles as potential models for rapidly estimating Sw for reservoir characterization. A broader field application of the proposed methodologies is expected to provide greater insight into subsurface fluid distribution thereby improving hydrocarbon recovery.