Field/project development
Plans call for license partner Aker BP to serve as operator during the development phase, with operatorship reverting to DNO after first oil in 2028.
In lifting force majeure, TotalEnergies says it will restart construction on its Mozambique LNG project as soon as the government agrees to a revised budget and schedule which targets shipping first product in 2029.
Supermajor aims to start appraisal activities in 2027 and is evaluating development possibilities for the deepwater find offshore Brazil, which is the company’s largest discovery in 25 years.
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Machine learning has been shown to have a promising role in oil and gas explorations in recent years. Among the applications, determining a proper location for injection and production wells along with their optimal operating conditions is a complex problem. -
Suriname's wait to become a significant oil producer may be nearing an end as the French supermajor begins early development studies.
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The discovery in the US Gulf of Mexico was tied back to the existing Shenzi tension-leg platform.
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High demand for offshore services makes for robust charter rates.
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The Advanced Clean Energy Storage project in Utah involves two 4.5-million-bbl salt caverns that will store up to 100 metric tons of hydrogen per day.
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Work slated for Côte d’Ivoire and Italy includes new riser/pipelines and FSRU facilities.
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SponsoredThe data that comes with mapping flow behavior at the stage level of unconventional wells was once accessible only through the installation of costly and intrusive diagnostic methodologies like fiber optic or running production logging. New-generation FloTrac ultrahigh-resolution nanoparticle tracer technology with subatomic spectroscopic measurement techniques now de…
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The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.
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This paper summarizes a collaborative industry study to compare observations between shale-play data sets and basins, develop general insights into parent/child interactions, and provide customized economic optimization recommendations.
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In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.
