Digital Oil Field
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.
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
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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Despite the recent market conditions during the last year, our industry continued to demonstrate further advances in the monitoring and surveillance field and to attest to the added value provided. -
In the past decade, fiber-optic -based sensing has opened up opportunities for in-well reservoir surveillance in the oil and gas industry. In this paper, the authors present a recent example of single-phase-flow profiling with distributed acoustic sensing.
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Since 2007, an operator in Nigeria has registered a significant increase of oil-spill events caused by sabotage and oil-theft activities. The technology presented here allows detecting and locating leaks taking place at a distance from the sensor of up to 35 km.
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For thin-oil-rim reservoirs, well placement, well type, well path, and the completion methods must be evaluated with close integration of key reservoir and production-engineering considerations.
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Many business and digital corporations claim that between 100 billion and 200 billion devices could be connected by 2020.
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Permanent downhole gauges (PDGs) provide vast amounts of pressure-transient and rate data which may be interpreted with improved pressure-transient-analysis (PTA) approaches to gain more knowledge about reservoir dynamics.
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This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations.
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This paper describes the development of “digital-rocks” technology, in which high-resolution 3D image data are used in conjunction with advanced modeling and simulation methods to measure petrophysical rock properties.
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There is talk about digital oil fields and big data and some striking examples of their power. But in real oil fields, a lot of operators are still running fields with systems relying on big paper.
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A new intelligent model that successfully learns from high-dimensional data and effectively identifies high-production areas and optimum lateral-re-entry candidates is presented.
