AI-Driven Well Log Interpretation Methodology for the Optimization of Water Control and Carbon Footprint Based on Polymer Injection

Abstract

Abstract Energy-related sectors are making great efforts to guarantee the sustainability of their operations and lessen their carbon impact. Among these initiatives is field operation optimization to increase sustainability while preserving output levels. The oil and gas business is being greatly impacted by the fourth industrial revolution, which also makes it possible to analyze options for reducing carbon footprints more effectively. The formation can be precisely characterized, and its production behavior may be measured using modern production logging technologies. Technologies utilizing polymer injection have the ability to greatly reduce water cut, assisting in the reduction of carbon footprint. To model the effect of injecting polymers on the fluid production rates, we assessed the impact of several polymer injection techniques on the Volve field using a number of production and injection wells. The injection of polymers aids in lowering the reservoir's water production rates. This in turn lowers the carbon footprint associated with handling produced water and water injection. An important part of the optimization is the integration of well log interpreted polymer quantities to optimize the recovery. A probability-likelihood framework was used to calculate the overall carbon footprint after several scenarios were assessed to establish their widespread influence on carbon emissions. While the best injection technique may vary depending on a number of variables, polymers have shown to be capable of drastically lowering total carbon footprints while improving hydrocarbon output.