Digital Oil Field
The authors describe a study on key technologies for intelligent risk monitoring of workover operations.
The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across teams and vendors.
The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.
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The oil and gas industry is undergoing a significant shift with the advent of intelligent operations. This transformation is enabling upstream operations to move away from a reactive and manual mode of operation toward a more efficient, safe, and optimal state of operation. -
This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
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In this paper a case study is described in which a software solution enabled prescriptive optimization of well delivery using a physics-informed machine-learning approach for predictive identification and characterization of well-construction risks.
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The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
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This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
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This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
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Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.
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This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.
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This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
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The objective of this study is to field test a non-nuclear multiphase flowmeter and assess its performance under challenging operating conditions.
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