Digital Oil Field
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.
The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
The authors describe a study on key technologies for intelligent risk monitoring of workover operations.
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across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.
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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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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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