Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

Abstract

Accurate prediction of pore-pressures in the subsurface is paramount for successful planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid drilling problems. As it is expensive and time-consuming to measure pore-pressure directly in wellbores, it is useful to be able to predict it from various petrophysical input variables on a supervised learning basis calibrated to a benchmark wellbore. This study developed and compared three-hybrid machine-learning optimization models applied to a diverse suite of 9 petrophysical input variables to predict pore-pressure across a 273-m-thick, predominately carbonate, reservoir sequence in the giant Marun oil field (Iran) using 1972 data records. The analysis identified that the multilayer extreme learning machine model hybridized with a particle swarm optimization (MELM–PSO) applied to seven input variables by feature selection provided the most accurate pore-pressure predictions for the full dataset (RMSE = 11.551 psi (1 psi = 6.8947590868 kPa) for well MN#281). The Savitzky–Golay (SG) filter was applied to pre-process the data, and the properties were filter-ranked using the wrapping method. The MELM–PSO model outperformed the pore-pressure prediction accuracy achieved by commonly used empirical formulas involving sonic or resistivity log data or calculated pore compressibility. To further verify and generalize the applicability of the MELM–PSO model, it was applied to two other Marun oil field wells (MN#297 and MN#378) achieving RMSE prediction accuracy of 10.031 psi and 10.150 psi, respectively. These results confirmed that the trained model can be reliably applied to multiple locations across the Marun oil field for predicting pore-pressure.