The presence of silos in data and technology of the oil and gas (O&G) production value chain prevents the optimal utilization of resources to enhance production, improve efficiency, and reduce carbon emissions in the O&G production value chain. Real-time optimization of O&G production value chain (ROOPVC) can be used to achieve the above-described objectives. Specifically, ROOPVC allows for i) integration of various elements of the O&G production value chain to create a single reference truth of the system, ii) prediction of unified behavior of the single reference truth using physics-based models and data-driven algorithms, and iii) holistic optimization via single unified digital twin (DT).

Based on recent advances, this study reviews system-level and component-level technologies required to implement ROOPVC. Specifically, the study reviews in detail the two major elements of ROOPVC, which are i) DT technology and ii) modeling, simulation, and optimization, respectively. The study also summarizes field experiences in the deployment of ROOPVC. The key challenges, lessons learned, and recommendations for the deployment of ROOPVC are also discussed.

The major findings from this review suggest that ROOPVC i) can enable higher stable production while simultaneously allowing significant carbon savings, ii) is suitable for deployment on a field of any size, and iii) can be deployed quickly due to its modular (microservices) approach.

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