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Aging or depleted hydrocarbon reservoirs (AHRs or DHRs) represent a promising alternative for CO 2 geo-sequestration compared to other geological formations, owing to their distinctive characteristics and the availability of pre-existing infrastructure. However, large-scale deployment faces complex, multidimensional challenges that require ongoing research to ensure optimal efficiency and safety. Despite notable progress in understanding the technical processes, significant techno-economic barriers remain. To overcome these obstacles, it is essential to adopt a critical and holistic analysis of existing studies while also exploring innovative approaches. Most recent reviews, though contributing significantly, have focused on specific aspects of CO 2 storage in these reservoirs, neglecting a systemic and multidimensional approach that integrates these various challenges into a single analysis. This fragmented approach leaves a gap in the literature, which may result in an incomplete understanding of the complex interactions between different factors, reducing the effectiveness of proposed solutions and limiting the ability to anticipate long-term impacts on the safety and sustainability of sequestration systems. Additionally, the rapid evolution of technology and scientific knowledge necessitates a constant update of studies related to sequestration in DHRs. Incorporating the latest technological innovations and methodological approaches is crucial to optimizing carbon capture and storage (CCS) processes, enhancing long-term safety, and adapting reservoir management strategies to increasing environmental and economic constraints. This review aims to address these gaps by providing a critical, comprehensive, and multidimensional analysis of recent advances while identifying persistent challenges. The integration of technical, economic, and environmental dimensions into a unified perspective offers a strategic global vision essential for guiding future research and supporting industrial applications. Furthermore, synthesizing the most recent developments and highlighting areas requiring further investigation, this study outlines a strategic roadmap for optimizing CO 2 sequestration in AHRs and DHRs, offering crucial insights for both research and industrial innovation.
Aging or depleted hydrocarbon reservoirs (AHRs or DHRs) represent a promising alternative for CO 2 geo-sequestration compared to other geological formations, owing to their distinctive characteristics and the availability of pre-existing infrastructure. However, large-scale deployment faces complex, multidimensional challenges that require ongoing research to ensure optimal efficiency and safety. Despite notable progress in understanding the technical processes, significant techno-economic barriers remain. To overcome these obstacles, it is essential to adopt a critical and holistic analysis of existing studies while also exploring innovative approaches. Most recent reviews, though contributing significantly, have focused on specific aspects of CO 2 storage in these reservoirs, neglecting a systemic and multidimensional approach that integrates these various challenges into a single analysis. This fragmented approach leaves a gap in the literature, which may result in an incomplete understanding of the complex interactions between different factors, reducing the effectiveness of proposed solutions and limiting the ability to anticipate long-term impacts on the safety and sustainability of sequestration systems. Additionally, the rapid evolution of technology and scientific knowledge necessitates a constant update of studies related to sequestration in DHRs. Incorporating the latest technological innovations and methodological approaches is crucial to optimizing carbon capture and storage (CCS) processes, enhancing long-term safety, and adapting reservoir management strategies to increasing environmental and economic constraints. This review aims to address these gaps by providing a critical, comprehensive, and multidimensional analysis of recent advances while identifying persistent challenges. The integration of technical, economic, and environmental dimensions into a unified perspective offers a strategic global vision essential for guiding future research and supporting industrial applications. Furthermore, synthesizing the most recent developments and highlighting areas requiring further investigation, this study outlines a strategic roadmap for optimizing CO 2 sequestration in AHRs and DHRs, offering crucial insights for both research and industrial innovation.
Summary Intensive growth of geological carbon sequestration has motivated the energy sector to diversify its storage portfolios, given the background of climate change mitigation. As an abundant unconventional reserve, shale gas reservoirs play a critical role in providing sufficient energy supply and geological carbon storage potentials. However, the low recovery factors of the primary recovery stage are a major concern during reservoir operations. Although injecting CO2 can resolve the dual challenges of improving the recovery factors and storing CO2 permanently, forecasting the reservoir performance heavily relies on reservoir simulation, which is a time-consuming process. In recent years, pioneered studies demonstrated that using machine learning (ML) algorithms can make predictions in an accurate and timely manner but fails to capture the time-series and spatial features of operational realities. In this work, we carried out a novel combinational framework including the artificial neural network (ANN, i.e., multilayer perceptron or MLP) and long short-term memory (LSTM) or bi-directional LSTM (Bi-LSTM) algorithms, tackling the challenges mentioned before. In addition, the deployment of ML algorithms in the petroleum industry is insufficient because of the field data shortage. Here, we also demonstrated an approach for synthesizing field-specific data sets using a numerical method. The findings of this work can be articulated from three perspectives. First, the cumulative gas recovery factor can be improved by 6% according to the base reservoir model with input features of the Barnett shale, whereas the CO2 retention factor sharply declined to 40% after the CO2 breakthrough. Second, using combined ANN and LSTM (ANN-LSTM)/Bi-LSTM is a feasible alternative to reservoir simulation that can be around 120 times faster than the numerical approach. By comparing an evaluation matrix of algorithms, we observed that trade-offs exist between computational time and accuracy in selecting different algorithms. This work provides fundamental support to the shale gas industry in developing comparable ML-based tools to replace traditional numerical simulation in a timely manner.
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