Digital oilfield
The paper describes the deployment of fiber-optic monitoring of CO₂ injection and containment in a carbonate saline aquifer onshore Abu Dhabi.
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Intelligent completions could improve many of the world’s oil and gas wells, but not all are suited to the technology. There is another option.
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Kongsberg Digital’s mobile companion to the SiteCom platform is designed to keep wellsite insight close at hand, wherever the job takes you.
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Artificial intelligence is already part of the work done in an office near you, and, before you know it, it will be in your office as well. Gaining familiarity and an understanding of it will serve you well. -
The future of intelligent operations in our industry is being driven by advances from other sectors that have been embraced for petroleum applications. Foundational changes already taking place include advances in the type and volume of data being acquired and how the data are used.
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The complete paper presents a discussion of the use of intelligent well completion in Santos Basin Presalt Cluster wells.
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In partnership with TechnipFMC, DNV GL opened a pilot project for the international collaboration of operators and the supply chain. Many digital twins represent an asset’s initial form and struggle to reflect developments in their physical counterparts as the asset matures.
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Edge—or, in-field, device-level—computing is being driven by the need for data from individual wells to be analyzed and processed at the wellsite instead of in data centers for early and accurate decision making.
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Immersive technology on projects such as wellsite planning incorporates XR, drones, and collaboration.
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Production monitoring requires heightened degrees of precision and efficiency as operations are streamlined and projects are evaluated continuously. This month’s feature focuses on innovative technologies that have been implemented in environments ranging from the Gulf of Mexico to China.
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This paper describes a virtual metering tool that can monitor well performance and estimate production rates using real-time data and analytical models, integrating commercial software with an optimization algorithm that combines production and reservoir information.
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This paper discusses the effectiveness of third-generation (Gen3) production-logging-tool (PLT) technology, which uses co-located digital sensors for simultaneous acquisition of flow data to provide the most accurate characterization of the flow condition at each depth surveyed.
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This paper describes an accurate, three-step, machine-learning-based early warning system that has been used to monitor production and guide strategy in the Shengli field.
