Neural Network Models for Predicting Wellhead Pressure-Flow Rate Relationship for Niger Delta Oil Wells

Abstract

Some wellhead pressure - flow rate correlations developed for Niger Delta region oil wells are in-house estimation tool by the operating companies in this region. However, the limited available correlations for wellhead pressure - flow rate prediction for Niger Delta oil wells are not generalized. A more robust and adaptable soft computing approach - Artificial Neural Network (ANN) was developed to address the inconsistency using field test data: production flow rate (q), flowing wellhead pressure (Pwh), choke size (S), gas-liquid ratio (GLR), flowing temperature (FTHP) and basic sediments and water (BS&W) obtained from 64 oil wells in Niger Delta fields. The developed ANN models were based on Gilbert and modified Gilbert forms of equation for predicting wellhead pressure - flow rate relationship. The results obtained indicate that the developed ANN models resulted in accurate predictions than the empirical correlations. The statistical analysis of the developed ANN models predictions with the field test data also resulted in average error, absolute relative error, root mean error and standard deviation of -0.1233, 0.1920, 0.3650 and 0.3621 for Gilbert form and -0.0450, 0.1045, 0.4533 and 0.4498 for modified Gilbert form, respectively. The results also show that the ANN models’ prediction resulted in coefficient of determination (R2) of 0.9653 and 0.9951 for Gilbert and modified Gilbert respectively. The developed ANN models for Gilbert and modified Gilbert predictions are close with coefficient of determination (R2) of 0.9619. Therefore, the ANN models are superior to the empirical correlations’ predictions for wellhead pressure and can be used as a quick-and-robust tool for oilfield prediction of wellhead pressure - flow rate relationship in Niger Delta oil fields.