Dynamic risk modeling of complex hydrocarbon production systems

Abstract

This study presents a dynamic risk modeling strategy for a hydrocarbon sub-surface production system under a gas lift mechanism. A data-driven probabilistic methodology is employed to conduct a risk analysis. The integrated approach comprises a multilayer perceptron (MLP) – artificial neural network (ANN) model and a Bayesian network (BN) technique. The MLP-ANN model performs the production forecast, and the BN model analyzes dynamic risks (the production response) and evaluates the impact of the sand face pressure on risks. The introduced model offers an effective strategy to avoid production failure and to monitor dynamic risks. The dynamic risk analysis yields predictive outcomes at any production time in the well’s production life. It offers field operators an early warning system based on the Bayesian model with prognostic capabilities. The proposed strategy effectively manages production risks and assists in production decision-making, especially in complex production systems.