Asset Management
Talos Energy founder Tim Duncan has been named executive chairman of newly formed 1947 Oil&Gas, which will focus on acquiring and developing mature, shallow-water assets through its buyout of Renaissance Offshore. The deal is expected to close in Q2 2026.
Operators aren’t rushing to drill, even as the closure of the Strait of Hormuz drives oil prices up.
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. -
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across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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The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
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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.
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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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EQT is benchmarking its way to basin-leading productivity and relying on partnerships and new technology to turn KPIs into operational reality.
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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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