Enhancing reservoir porosity prediction from acoustic impedance and lithofacies using a weighted ensemble deep learning approach

Abstract

Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consuming, expensive, and difficult to conduct. In addition, seismic inversion confronts problems of nonlinearity and has multiple solutions. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability, mapping directly from acoustic impedance and lithofacies data to porosity. To prove the point, in this paper, we trained an ensemble of DL models and then proposed a weight combination of every single trained model’s strength to improve the result. We evaluated the method's reliability using a number of metrics. Further, we compared it with traditional ones. The weighted ensemble resulted in a lower error than the simple ensemble and the single model. Its spatial distribution map showed the best connectivity with that of historical porosity. Finally, we tested our method's effectiveness using a dataset that was used in a previously published study. Our method improved the prediction of the latter.

Keywords

• Machine learning
• Deep neural networks
• Reservoir rock properties
• Ensemble learning methods