Optimizing Petroleum Reservoir Management via Generative AI for Adjusted Decision-Making Process in Revitalizing Mature Basins of Pakistan

Abstract

The aim of this study is to investigate the application of generative AI techniques for improving petroleum reservoir management practices. The results of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.

Initially an AI model is trained on the input data sets for the reservoir, including geological, geophysical, and production data. The consistency, quality, and compatibility of the data was ensured by using generative AI algorithms. The refined data set is then used to create realistic reservoir models, simulate fluid flow, and predict reservoir behavior under different operating conditions. The generated outcomes are validated against historical data and traditional calculations and the same outcomes are evaluated by comparing hydrocarbon recovery, production rates, and economic returns. Finally, sensitivity analysis is conducted to assess the impact for revitalizing Mature Basins of Pakistan.

The results of this study show that the accuracy of generative AI can be improved by developing a model based on algorithms tailored for reservoir management tasks. Training of the model was focused on various reservoir parameters adopted from reservoir and production data out of which 74% response was initiated from geological and geophysical section, however the non-pay zone fluid section was involved only 3%. The model was tested to improve the accuracy for which accuracy overlapping chart was plotted. It was observed that reservoir models were perfectly aligned with base case results and simulated fluid flow showed highest difference of 150 BOPD in recovery from Potwar Basins of Pakistan. The reservoir behavior prediction showed moderate difference. The reason for deflection was not considering the computational fluid dynamics for model development. In conclusion, the improved accuracy of generative AI models has potential to resolve time and economic challenge of reservoir management.

The significance of this study is based on improvisation of Decision-Making Process and possess potential to revolutionize the petroleum industry. Primarily, as the accuracy of models will improve the decision-making process will be based on more accurate predictions, optimized production strategies, and real-time insights. Secondary, accuracy-oriented AI outcomes will reveal the effectiveness of generative AI techniques in reservoir management which will influence in revitalizing the mature basins of Pakistan by adoption of generative AI model applications.