Field/project development
The companies will work together to increase future production and value creation at their respective assets in the Norwegian Continental Shelf.
After a decade and a half of declining production, Uzbekistan, ranked 15th worldwide in gas output, has been seeking foreign partners to revive and reverse the fortunes of its oil and gas industry.
While often associated with smaller discoveries, subsea tiebacks are playing a growing role in contributing to the broader energy mix.
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AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution. -
Hydraulic fracturing holds great potential in the region, but there are several key questions worth asking as efforts move forward.
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The following three papers show challenges and potential solutions across various stages of the deepwater well-development cycle from a variety of deepwater basins across the world.
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The authors describe a study on key technologies for intelligent risk monitoring of workover operations.
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Formerly titled E&P Notes, this monthly snapshot of global E&P activity highlights ongoing developments worldwide.
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This paper establishes that the use of a dual-gradient fluid column during the running of large casing in an extreme-reach deepwater well is an effective method to overcome drag and enable the casing to reach total depth.
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across teams and vendors.
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The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.
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This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
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In this paper, a case study is described in which a software solution enabled prescriptive optimization of well delivery using a physics-informed machine-learning approach for predictive identification and characterization of well-construction risks.
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