Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques

Abstract

Abstract Flowing bottom-hole pressure (FBHP) is a key metric parameter in the evaluation of performances of oil and gas production wells. An accurate prediction of FBHP is highly required in the petroleum industry for many applications, such the hydrocarbon production optimization, oil lifting cost, and assessment of workover operations. Production and reservoir engineers rely on empirical correlations and mechanistic models exist in open resources to estimate the FBHP. Several empirical models have been developed based on simulation and laboratory results that involved many assumptions that reduce the model s accuracy when they are applied for the field applications. The technologies of machine learning (ML) are one discipline of Artificial Intelligence (AI) techniques provide promising tools that help solving human s complex problems. This study develops machine-learning based models to predict the multiphase FBHP using three machine learning techniques that are Random forest, K-Nearest Neighbors (KNN), and artificial neural network (ANN). Results showed that using an artificial neural network model give error of 2.5% to estimate the FBHP which is less than the random forest and K-nearest neighbor models with error of 3.6% and 4% respectively. The ML models were developed based on a surface production data, which makes the FBHP is predicted using actual field data. The accuracy of the proposed models from ML was evaluated by comparing the results with the actual dataset values to ensure the effectiveness of the work. The results of this study show the potential of artificial intelligence in predicting the most complex parameter in the multiphase petroleum production process.