A robust pressure drop prediction model in vertical multiphase flow: a machine learning approach

Predicting pressure drop in multiphase flow is crucial for optimizing tubing size, wellhead pressure (WHP), completion design, cost management, and other production-phase objectives. While various correlations and models exist to calculate pressure drops, their accuracy diminishes significantly when applied to outlier datasets beyond their intended parameter ranges. Not only that, as reported by many studies, most of the empirical correlations and mechanistic models have an error, which is quite high and intolerable. This study introduces a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for accurately predicting pressure drops in vertical wells carrying multiphase fluids. A comprehensive dataset of 335 experimental records was compiled from diverse sources to encompass a wide range of parameters, ensuring the robustness of the proposed model. Key input parameters include WHP, oil rate, water rate, gas rate, inner diameter, surface temperature, and flow length. The ANFIS model was rigorously evaluated using different methods, such as cross plot, error distribution, Kruskal-Wallis (KW) test, error boxplot and violin graphs, Confidence Interval (CI), and statistical error metrics. The latter includes Average Absolute Percentage Error (AAPE), Root Mean Square Error (RMSE), and the coefficient of determination (R²) to prove the ANFIS model’s performance. The proposed ANFIS model was compared with the most commonly used published models. Results demonstrate that the ANFIS model outperforms existing methods, achieving an AAPE of 2.92%, an RMSE of 1.9638%, and an R² of 0.9645. KW test, error boxplot, violin graphs, and CI results indicated that the best model for predicting pressure drops is the proposed ANFIS model. These findings establish the ANFIS model as a reliable and superior tool for predicting pressure drops in vertical wells with multiphase flow, offering significant advancements in design accuracy and operational efficiency.