Risk management
Barzan plant explosion that killed 13 and injured 66 occurred 2 days after production was restarted following a scheduled shutdown in December for routine maintenance.
This article from the SPE Integrated Reservoir Management Technical Section (IRMTS) addresses decision timing across the reservoir cycle.
This guest editorial from the Center for Injection and Seismicity Research (CISR) at The University of Texas at Austin details the emerging risks posed by injection in Texas and what steps might be taken to mitigate them.
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The SPE Asset Management Technical Section (AMTS) is teaming up with the University of Houston to launch a pilot program that advances asset management practices and supports the growth of energy professionals worldwide—setting a model for future collaborations between universities and SPE technical sections.
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The SPE Management Technical Section has been officially renamed the Asset Management Technical Section. The new name better reflects the section’s focus on asset management as a core discipline in the upstream oil and gas industry.
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This paper describes a tool that complements predictive analytics by evaluating top health, safety, and environment risks and recommends risk-management-based assurance intervention.
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Technical safety standards are essential as global offshore exploration heats up.
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The Ohio River Valley Institute finds operator indifference, regulatory noncompliance as chief factors in state's growing number of deserted wells.
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Buoy-based camera footage, analyzed by artificial intelligence, can help reduce the risk of birds colliding with offshore wind farm turbines.
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EVP Giulia Chierchia and Chairman Helge Lund are stepping down amidst difficult Q1.
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The EIA issued a new forecast for US oil production as a result of US President Donald Trump’s recently announced tariffs.
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A 25% tariff on steel imported into the US has some US oil and gas companies nervous about future spending plans.
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This paper introduces a novel optimization framework to address CO2 injection strategies under geomechanical risks using a Fourier neural operator-based deep-learning model.