Machine learning model for constant volume depletion prediction of the Niger Delta condensate systems

Abstract

Accurate assessment and evaluation of condensate reservoir performance is dependent on the depth of study of the condensate systems. Due to the anomalous behavior of these reservoirs, a great deal of knowledge is required to accurately forecast performances associated with these systems. Three machine learning (ML) models with proven capabilities and accuracy were developed based on Extratrees (ET), Adaptive Boosting (Adaboost) and Gradient Boosting algorithm (GBM). These models were used to exploits various parameters such as, gas composition, C7+ fraction properties, depletion pressure steps, and reservoir temperature. The data points stretching through different samples from the Niger Delta that passed the quality control tests were used in the validation of the proposed model for this study. Using various metrics to evaluate the accuracy of the models, analyzing the result of two samples A and B show that algorithm with R2 of 0.9868 and 0.9951, MAPE of 0.21224 and 0.10984, RMSE of 0.01746 and 0.01697, MAE of 0.0128 and 0.01457 for sample A and B respectively. Algorithm presents a more accurate prediction when compared with Adaboost and GBM models for two samples. The results from this study can be uniquely serialized and deployed into PVT simulation packages