Asset Management
Acting director of the new center Ale Hakala outlines the research priorities guiding the newly established center’s focus on production enhancement technologies.
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
The acquisition establishes a unified North American upstream analytical data set with the goal of streamlining capital allocation decisions.
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Updates about global exploration and production activities and developments. -
This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for carbon-dioxide enhanced oil recovery and storage.
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Only about one-third of Africa’s discovered hydrocarbon resources have reached commercialization.
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The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.
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Experience in subsurface production and lift design is shaping a new generation of geothermal operations built for reliability and scalability.
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This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
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This study identifies critical knowledge gaps in wellbore integrity and underscores areas that require further investigation, providing insights into how wellbores must evolve to meet the technical demands of the energy transition.
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This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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This study illustrates the new capabilities, tailored for carbon-dioxide storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells.
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This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.
