Development and Validation of an Empirical Correlation for Water Production Forecasting in Petroleum Reservoirs

Abstract

Accurate water production prediction is critical for field development optimization, surface facility design, and production management in mature oil reservoirs. This study develops empirical correlations for forecasting Water-Oil Ratio (WOR) using 9,161 production records from seven Volve Field wells (Norwegian North Sea, 2007–2016). Four approaches were evaluated: multiple linear regression, power law correlation, polynomial regression, and an exponential model, benchmarked against established methods including the X-Plot, Ershaghi-Omoregie, Buckley-Leverett, Arps decline curve, and Chan diagnostic techniques. Feature engineering generated derived variables including cumulative oil production, pressure ratio, production time, gas-oil ratio, and productivity index. After removing non-physical values and treating extreme WOR observations, data were split 80/20 for training and validation. The power law correlation achieved the strongest test-set performance (R2 = 0.845, RMSE = 2.374, MAE = 1.065), expressing WOR as a function of cumulative oil production, pressure ratio, and production time. It outperformed all conventional benchmarks, with the Arps decline-based method representing the best traditional comparator but at substantially lower accuracy. These results demonstrate that simple empirical correlations, when derived from high-quality datasets, can reliably forecast water production behavior. The proposed correlation provides a practical, easily implemented tool for production forecasting, water handling capacity planning, and operational decision-making within standard reservoir engineering workflows.