Minimizing Carbon Footprint by Smart Sustainable Reservoir Management

Abstract

Abstract Sustainability and reducing carbon footprint has attracted attention in the oil and gas industry to optimize recovery and increase efficiency. The 4th Industrial Revolution has made an enormous impact in the oil and gas industry and on analyzing carbon footprint reduction opportunities. This allows classification of various reservoir operations, installation of permanent sensors and robots on the field, and reduction of overall power consumption. We present an overview of new AI approaches for optimizing reservoir performance while reducing their carbon footprint. We will outline the significant carbon emissions contributors for field operations and how their impact will change throughout the production's lifecycle from a reservoir. Based on this analysis, we will outline via an AI-driven optimization framework areas of improvement to reduce the carbon footprint considering the uncertainty. We analyzed the framework's performance on a synthetic reservoir model with several producing wells, water, and CO2 injecting wells. Beneficial in reducing carbon emissions from the field is the reuse and injection of CO2 for enhancing hydrocarbon production from the reservoir. One hundred different scenarios were then investigated utilizing an innovative autoregressive network model to determine the impact of these components on the overall carbon emission of the field and determine its uncertainty. The conclusions from the analysis were then incorporated into a data-driven optimization routine to minimize carbon footprint while maximizing reservoir performance. The final optimization results of the showcase outlined the ability to reduce the carbon footprint significantly.