Neuro-Adaptive Learning Approach for Predicting Production Performance and Pressure Dynamics of Gas Condensation Reservoir

Abstract

In Reservoir Engineering, state-of-the-art data analysis enables engineers to characterize reservoirs and plan for developing fields. This allows production companies to save huge amounts that would otherwise be allocated to reservoir modelling and simulation and well testing. Numerical reservoir modelling and simulation is the standard use in industries today for comprehensive study of fields. However, the inflexible behaviour, development time and cost of numerical simulators are major challenges to production engineers, managers as well as modellers. On the other hand, Artificial Intelligence (AI) based reservoir models are characterised with low cost of development, short development time and fast tract analysis and flexibility to estimate the uncertainties normally found in numerical simulators. In this study, a strategy for controlling gas production and pressure drop in gas condensate reservoir is described. Numerical simulations of production rate and pressure drop were carried out first and their results were saved. An Adaptive Neuro-Fuzzy system was then developed and trained with some parts of the numerical simulation results. This AI-based system is checked and tested with part of the numerical simulation results that had not been used during the training. The developed system regenerates the numerical simulation results for both production rates and pressure drop at different Bottom Hole Pressures (BHPs) with very high accuracy (>98%). Results of this study showed that AI-based reservoir simulation can be considered a vital tool of help to production engineers, managers and modellers for a quick and more informed decision as regards field development plans that can meet operational targets.