An Innovative Artificial Intelligence Framework for Reducing Carbon Footprint in Reservoir Management

Abstract

\n The energy industry has been transformed considerably in the last years. Sustainable development of oil and gas reservoir has become a major driver for these energy companies, and strengthened the focus to maximize hydrocarbon extraction while minimizing the associated carbon footprint. The focus has been further on maximizing efficiency and waste reduction in order to enhance profitability of projects. Challenges still remain in terms of that the carbon emissions from oilfield operations, related to the production, disposal and utilization of water and hydrocarbons, may be significant and the objective of increasing production has to be traded off in many instances against the quest for reducing carbon emissions. The fourth industrial revolution has brought new opportunities for companies to enhance decision making in their upstream development and optimize their recovery potential while minimizing the carbon footprint and associated cost.\n In this work, we present a smart approach for optimizing recovery while minimizing the carbon footprint of a reservoir in terms of the associated development and production activities. We use an advanced nonlinear autoregressive neural network approach integrated with time-lapse electromagnetic monitoring data to forecast production and carbon emissions from the reservoir in real-time, under uncertainty. The artificial intelligence approach also allows to investigate a circular carbon approach, where the produced greenhouse gases are re-injected into the well, while at the same time adjusting water injection levels. This allows to forecast and analyze the impact of a circular development plan.\n We tested the AI framework on a synthetic reservoir encompassing a complex carbonate fracture system and well setup. The carbon emissions were forecasted in real-time based on the previous production rates and the defined injection levels. The forecasted carbon emissions were then integrated into an optimization technique, in order to adjust injection levels to minimize water cut and overall carbon emissions, while optimizing production rates.\n Results were promising and highlighted the potential significant reductions in carbon emissions for the studied synthetic reservoir case. Moreover, the deployment of deep electromagnetic surveys was proved particularly beneficial as a deep formation evaluation monitoring method for tracking the injected waterfront inside the reservoir and optimizing the sweep efficiency, while minimizing the inefficient use of water injection. Accordingly, such integrated AI approach has a twofold benefit: maximizing the hydrocarbon productivity, while minimizing the water consumption and associated carbon emissions.\n Such framework represents a paradigm shift in reservoir management and improved oil recovery operations under uncertainty. It proposes an innovative integrated methodology to reduce the carbon footprint and attain a real-time efficient circular development plan.