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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Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data. -
Researchers from Skoltech have trained a neural network to recognize rock samples in core box images efficiently. The process has sped up analysis by up to 20 times and made it possible to automate the description of rock samples.
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Chief digital and information officer Sandeep Gupta's innovative use of technology has enabled the company to cut costs, reduce time to first oil, and manage decline in production.
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The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.
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The collaboration is planned to explore artificial intelligence in an effort to get more value from oil and gas operations and create a sustainable and carbon-efficient future for the energy industry.
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Accenture research suggests that only 12% of companies have advanced their AI maturity enough to achieve superior growth and business transformation. These companies are "AI Achievers" and, on average, attribute 30% of their total revenue to AI.
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Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.
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The paper describes the experience of using a machine-learning model prepared by the ensemble method to prevent stuck-pipe events during well construction in extended-reach wells.
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This paper presents the development and test of a method to predict upstream events that could lead to flaring, applying an integrated framework using machine-learning and big-data analytics.
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A field test conducted by Yokogawa Electric and JSR resulted in a chemical plant being run autonomously for the first time by artificial intelligence for 35 days.
