Prediction of oil rates using Machine Learning for high gas oil ratio and water cut reservoirs

Abstract

Allocated well oil rates are essential well performance evaluation. Flow meters are not reliable at high gas-oil ratio (GOR) and high water-cut (WC). Most of the available formulas are based on Gilbert-type formulas with neglecting the differential pressure across the choke. Adaptive network-based fuzzy inference system (ANFIS), and functional networks (FN) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (550 wells) was obtained from oil fields in the Middle East. GOR varied from 1,000 to 9,351 scf/stb, WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. The developed AI models were compared against the previous published formulas. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the subcritical flow for ANFIS and FN were 1.25, and 0.95%, respectively. While in the critical flow, the AAPE values were 1.1, and 1.35% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas by 34%. The findings from this study will significantly assist production engineers to predict the oil rate in real-time without adding any cost or field intervention.