Digital Oil Field
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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Investment in digital technologies may increase project efficiency and reduce costs. However, Technip’s chief executive officer (CEO) said it is equally important to strengthen relationships with companies along the supply chain. -
Visualization technology has played a key role in reducing operational expenditure (OPEX) and improving collaboration, thus maximizing uptime across the industry throughout the asset life cycle.
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A recently released report predicted that IoT networks will not take up as much of the overall market share as previously anticipated.
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Distributed temperature sensing (DTS) is the most common fiber-optic measurement used for steam-assisted-gravity-drainage reservoir monitoring.
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Although early inflow control devices and intelligent completions (ICs) were introduced almost 20 years ago, completion technology has not kept pace with advancements in drilling technology.
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As the drilling industry improves its efforts to capture drilling operation activities in real time, it has generated a significant amount of data that drilling engineers cannot process on their own.
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Researchers at Heriot-Watt University in Edinburgh, Scotland, are building replica core samples using 3D printers and installing sensors inside them as they go. Their goal is to directly monitor pore-scale flow behavior from the inside of these so-called “smart rocks.”
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A real-time method is presented to predict impending stuck pipe with sufficient warning to prevent it. The new method uses automated analysis of real-time modeling coupled with real-time-data analysis.
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Over the last couple of decades, we have seen a steady stream of “intelligent” innovations go from ideas to infancy to catalog solutions.
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Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides extensive information about the reservoir. In this work, a machine-learning framework based on PDG data was extended to two applications: multiwell testing and flow-rate reconstruction.
