Field/project development
The Loran Phase 2 license award builds on BP’s growing presence in Venezuela as it seeks to develop offshore projects, particularly shared fields near Venezuela’s maritime border with Trinidad and Tobago.
ExxonMobil and its Area 4 partners have awarded $1.1 billion in pre-investment contracts for long-lead upstream equipment supporting Mozambique's Rovuma LNG project. The awards, led by OneSubsea, mark a key step toward FID as development advances on ExxonMobil's largest global investment.
Gran Tierra will refocus its investment strategy to strengthen its portfolio in Canada and Azerbaijan while Maurel & Prom, majority owned by Pertamina’s international upstream arm, grows its South American footprint in Colombia and Ecuador.
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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.
