A Novel Artificial Intelligence Framework for the Optimal Control of Wireless Temperature Sensors for Optimizing Oxygen Injection in Subsurface Reservoirs

Abstract

Abstract Hydrogen has become a very promising green energy source and it has the potential to be utilized in a variety of applications. Hydrogen, as a power source, has the benefits of being transportable and stored over long periods of times, and does not lead to any carbon emissions related to the utilization of the power source. Thermal EOR methods are among the most used recovery methods. They involve the introduction of thermal energy or heat into the reservoir to raise the temperature of the oil and reduce its viscosity. The heat makes the oil mobile and assists in moving it towards the producer wells. The heat can be added externally by injecting a hot fluid such as steam or hot water into the formations, or it can be generated internally through in-situ combustion by burning the oil in depleted gas or waterflooded reservoirs using air or oxygen. This method is an attractive alternative to produce cost-efficiently significant amounts of hydrogen from these depleted or waterflooded reservoirs. A major challenge is to optimize injection of air/oxygen to maximize hydrogen production via ensuring that the in-situ combustion sufficiently supports the breakdown of water into hydrogen molecules. which can then be separated from other gases via a palladium copper alloy membrane, leaving clean blue hydrogen. A crucial challenge in this process is achieving sufficient temperature in the reservoir in order to achieve this combustion process. The temperatures typically must reach around 500 degree Celsius to break the molecules apart. Hence, accurately monitoring the temperature within the reservoir plays a crucial role in order to optimize the oxygen injection and maximize recovery from the reservoir. Artificial intelligence (AI) practices have allowed to significantly improve optimization of reservoir production, based on observations in the near wellbore reservoir layers. This work utilizes a data-driven physics-inspired AI model for the optimal control of the high temperature wireless sensors for the optimal control of the oxygen injection in real-time. The framework was examined on a synthetic reservoir model with various producers and injectors. Each producer and injector contain various wireless high temperature sensors that are connected to each other. The framework then utilizes the temperature sensor data, in addition to the produced hydrogen, to optimize oxygen injection. This work represents a first and innovative approach to optimize subsurface wireless high temperature wireless sensing for maximizing hydrogen recovery from waterflooded reservoirs. The data-driven approach allows to optimize the hydrogen recovery representing a crucial element towards the drive for economical extraction of blue hydrogen.