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As the use of hydrogen gas (H2) as a renewable energy carrier experiences a major boost, one of the key challenges for a constant supply is safe and cost-efficient storage of surplus H2 to bridge periods with low energy demand. For this purpose, underground gas storage (UGS) in salt caverns, deep aquifers and depleted oil-/gas reservoirs has been proposed, which provide suitable environments with potentially high microbial abundance and activity. Subsurface microorganisms can use H2 in their metabolism and thus may lead to a variety of undesired side effects such as H2 loss into formation, H2S build up, methane formation, acid formation, clogging and corrosion. We present a new AI framework for the determination of metabolism processes of subsurface microorganisms in hydrogen underground storage. The AI framework enables to determine the potential microbial related processes and reactions in order to optimize storage strategies as well as incorporate potential remediating actions to ensure efficient and safe underground hydrogen storage and minimize related side effects. We evaluated the framework on investigating potential microbial reactions for hydrogen storage in the Pohokura gas field in New Zealand. The framework evaluates reservoir parameters, such as temperature, pressure, salinity and hydrogen injection volumes as well as duration, and then classifies which reactions may take place as well as indicates the likelihood of the reaction taking place. For the deep learning framework, an optimized random forest algorithm was implemented and compared to other multi-class classification problems. The results demonstrated the ability to determine the microbial reactions that may take place with hydrogen storage reservoir as well as its severity, which enhances the optimization of injection strategy as well as suitability of a reservoir. This framework represents an innovative approach to microbial reaction prediction for underground hydrogen storage. The framework allows potential reservoirs to be efficiently evaluated and optimization strategies to be utilized in order to maximize the efficiency of underground hydrogen storage.

CO2 plume geothermal technology (CPG) has been developed in recent years by several companies. The technology aims to utilize CO2 stored in saline aquifers to produce geothermal energy. CPG is different from conventional geothermal concepts. Here, the feedstock utilizes CO2 as a carrier fluid through which heat is extracted from the subsurface reservoir. Furthermore, the system does not necessarily rely on shallow natural hydrothermal locations but can utilize a conventional sedimentary basis. At last, CPG can still harvest energy in low-temperature environments that are currently not suitable for conventional geothermal extraction. We present a new deep learning optimization framework for the maximization of power generation from a CPG system. The framework utilizes an adapted N-BEATS approach. The approach is based on a stack of ensembled feedforward networks that are also stacked by interconnecting backcast and forecast links. The advantages of the framework are its flexibility with respect to different input parameters and various forecastable time series. This is particularly important for CPG to easily capture variations in the temporal dynamics and temperature responses across the various CO2 injection and production wells. We evaluated the framework on a simulated CO2 storage reservoir based in the Taranaki basin in New Zealand. The Taranaki basin has been well studied for CO2 storage, given the presence of a large saline aquifer that may be well suitable for both CO2 storage and CPG energy production. We simulated 3.5 years of CO2 injection and production for geothermal energy production as input to the N-BEATS framework. The training performance of the network was strong, and the model's performance was then evaluated on subsequent two years of energy production. The deep learning framework is then integrated into a global optimization framework to optimize energy production while adapting CO2 injection. The new deep learning N-BEATS optimization framework for CPG power generation represents an innovative way to enhance energy generation from CO2 storage reservoirs providing a sustainable way to minimize carbon footprint while delivering energy.

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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.

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We have presented a new deep learning framework for the detection of fractures in formation image logs for enhancing CO2 storage. Fractures may represent high velocity gas flow channels which may make CO2 storage a challenge. The novel deep learning framework incorporates both acoustic and electrical formation image logs for the detection of fractures in wellbores for CO2 storage enhancement and injection optimization. The framework was evaluated on the Pohokura-1 well for the detection of fractures, with the framework exhibiting strong classification accuracy. The framework could accurately classify the fractures based on acoustic and electrical image logs in 98.1 % for the training and 85.6 % for the testing dataset. Furthermore, estimates of the fracture size are strong, indicating the ability of the framework to accurately quantify fracture sizes in order to optimize CO2 injection and storage.