Asset Management
Operators aren’t rushing to drill, even as the closure of the Strait of Hormuz drives oil prices up.
Drilling experts recently shared candid views on what will be required for their segment of the upstream business to move to the next stage of development.
Hydraulic fracturing holds great potential in the region, but there are several key questions worth asking as efforts move forward.
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In partnership with OTC 2026, Rystad Energy has shared its latest outlook for the offshore sector and the role it is expected to play in supplying low-cost barrels through 2050. -
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across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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The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
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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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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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