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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Despite streams of data being available on platforms about the condition of topside and drilling equipment, most experts agree that only a small fraction of such data is used. Whether for a fleet or single platform, AI can transform an offshore enterprise. -
An AI-based application enabled operators to preempt ESP failures while optimizing production.
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Artificial intelligence systems can be trained to recognize visual content in drawings and provide a simplified context. The complete paper highlights the use of AI to process a scanned drawing and redrawing it on a digital platform.
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Artificial intelligence is already part of the work done in an office near you, and, before you know it, it will be in your office as well. Gaining familiarity and an understanding of it will serve you well.
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Time-stamped data anomalies can lead to more-accurate identification and faster diagnosis.
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"Sooner or later, we will get machines that are at least as intelligent as humans are," says Christof Koch, chief scientist and president of the Allen Institute for Brain Science in Seattle, Washington.
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Increasing accuracy in models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature-scaling methods of standardization and normalization and demonstrates when and how to apply each approach.
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This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights.
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Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input.
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The AI journey starts with a single step, but too many companies take the wrong first step.
