Enhancing CPG-Derived CO2 Flux Measurements via a Robust Wireless Sensor Analysis Framework – A New Zealand Taranaki Basin Case Study

Abstract In the pursuit of more sustainable resource use, lithium has emerged as a useful resource for a range of uses, including batteries. Due to these difficulties, there is a growing need for improved field operations that preserve output levels while also making the operations more sustainable. The Fourth Industrial Revolution are greatly impacting the oil and gas sector, and as the need for lithium to be used for batteries and energy technologies grows, hydrocarbon reserves are becoming a more desirable source of these precious minerals. Lithium has been found in considerable amounts in the generated brines of a number of reservoirs worldwide. In this study, we provide a novel artificial intelligence (AI) optimization strategy for optimizing lithium recovery from reservoir operations while preserving reservoir oil production goals. A deep learning LSTM algorithm is integrated into the AI framework to estimate water injection amounts based on oil, brine, and lithium recovery. After that, a global optimization framework using the deep learning model is integrated to optimize the water injection levels in order to maximize lithium recovery while preserving reservoir oil production levels. For the purpose of recovering lithium from reservoir brine in an oil and gas reservoir, we have presented a novel AI optimization framework. The framework allows hydrocarbon recovery rates to be maintained while optimizing lithium recovery. The framework, which highlights the potential for a large improvement in lithium recovery rates from an enhanced injection procedure, was successfully demonstrated on the Volve field. This paradigm has the potential to yield substantial benefits for optimizing the use of reservoir brine, which might lead to improved sustainability in reservoir operations.