Data & Analytics
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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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. -
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.
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This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
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This paper introduces a system that leverages sophisticated algorithms and user-friendly interfaces to tackle the challenge of developing complex, compartmentalized reservoirs effectively.
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This paper presents a novel application of artificial intelligence in computer vision for automating blowout-preventer pressure-chart-data extraction, demonstrating significant efficiency gains and a high return on investment.
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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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This paper details a data-driven methodology applied in Indonesia to enhance flare-emission visibility and enable targeted reduction strategies by integrating real-time process data with engineering models.
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Are we in an AI bubble? The question may seem academic to petroleum engineers who are already capitalizing on the momentum of digitalization across the industry, yet any engineer, regardless of their career stage, could be forgiven for feeling overwhelmed by the sheer scope of specialized skills now demanded in this rapidly evolving digital landscape.
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Switching from continuous circulation to cyclic huff-‘n’-puff operation in enhanced geothermal systems can significantly delay thermal breakthrough, sustain higher production temperatures, and improve long-term economic performance.
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The two companies say they plan to work together to use agentic AI to increase the capabilities of technical experts.
