Flow assurance
This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.
This paper explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.
This paper presents an approach to subsea hydrate-risk management based on the understanding that some crudes have induction properties that delay hydrate formation even when the pressure and temperature conditions reach the hydrate thermodynamic region.
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The authors of this paper describe a study of magnesian calcite particles forming hard, inhibitor-resistant calcium-carbonate-like deposits and potential mitigation measures. -
Flow assurance effects from slugging and various types of solids deposition during engineering, design, maintenance, and operations still create challenging technical issues needing safe, economical solutions. The recurring long-term mitigation of flow assurance phenomena such as the deposition of wax, erosion, asphaltenes, corrosion, and salt, still demands attention…
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Aramco investigates expanding the use of lasers in the upstream sector of the oil field.
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This case study demonstrates an estimated cost savings of AICD completions in six wells of more than $20 million in capital and operating expenses compared with a more conventional sliding side door completion to manage gas breakthrough.
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This paper is the second of a two-part series. It covers facilities problems caused by iron, injectivity problems caused by iron, and the mitigation of colloidal iron-related problems.
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The difficulty in selling a chemical to aid subsea pipeline flow has been delivering it to the wells.
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An integrated multidisciplinary work flow can improve the assessment of pipeline slugging fatigue for offshore field developments.
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Flow-assurance effects from slug-flow engineering, design, maintenance, and operations technical concerns still create and sustain challenging technical issues requiring safe, economical solutions for both onshore unconventional and offshore conventional production facilities.
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This paper describes a novel control scheme developed and tested on simulation for both hydrodynamic slugging and severe riser-induced slugging for a new facility in Angola.
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In the authors’ study, a machine-learning predictive model—boosted decision tree regression—is trained, tested, and evaluated in predicting liquid holdup in multiphase flows in oil and gas wells.
