AI/machine learning
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.
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The software that the duo is working on aims to optimize and automate the moving of drilling rigs. -
The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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This paper presents an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.
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We must admit that the oil field is still in the early days of its digital journey. It’s time to give serious thought to the expectation/reality gap, the cultural differences between the way we’ve always done things and the way that digital is changing us, and the pain points that may trip us up unless we’re careful.
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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.
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This article explains what deep learning is and how it works and presents an example use case from the energy industry.
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The agreement will put SLB’s Delfi software to work in Ineos’ oil and gas operations.
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This paper presents a family of machine-learning-based reduced-order models trained on rigorous first-principle thermodynamic simulation results to extract physicochemical properties.
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The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
