Machine Learning Based Prediction of PVT Fluid Properties for Gas Injection Laboratory Data

Abstract

Gas injection pressure-volume-temperature (PVT) laboratory data play an important role in assessing the efficiency of enhanced oil recovery (EOR) processes. Although typically there is a large conventional PVT data set, gas injection laboratory studies are relatively scarce. On the other hand, performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that gas injection is to be widely assessed as an optimal EOR process, there is increased demand on time- and cost-effective solutions to predict the outcome of associated gas injection lab experiments. While machine learning (ML) is extensively used to predict black-oil properties, it is not the case for compositional reservoir properties, including those related to gas injection. Can we use the typically extensive conventional laboratory data to help predict the needed gas injection parameters? This is the core of this paper. We present an ML-based solution that predicts pertinent gas injection studies from known fluid properties such as fluid composition and black oil properties. That is, learning from samples with gas injection laboratory studies and predicting gas injection fluid parameters for the remaining, much larger, data set. We applied the proposed algorithms on an extensive corporate-wide database. Swelling tests were predicted using the trained ML models for samples lacking gas injection laboratory data. Several ML models were tested, and results were analyzed to select the most optimal one. We present the algorithms and the associated results. We discuss associated challenges and applicability of the proposed models for other fields and data sets.