Training of machine learning algorithms for generating a reservoir digital twin

Abstract

Methods for training machine learning algorithms for generation of a reservoir digital twin include receiving information obtained from hydrocarbon wells. The information includes porosity logs, petrophysical data, rock typing data, pressure transient test results, vertical production logs, reservoir pressure logs, reservoir saturation logs, production performance, and injection performance. The reservoir saturation logs are normalized in accordance with time. A machine learning algorithm is trained using the reservoir pressure logs, the production performance, and the injection performance to provide variations in reservoir pressure of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained to provide variations in reservoir saturation of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained using the reservoir saturation logs, the vertical production logs, the production performance, the injection performance, the reservoir pressure logs, and the petrophysical data.