Enhanced recovery
The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.
This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
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This paper aims to present thoroughly the application of subsurface safety injection valves in extremely high-temperature environments. -
This paper presents a comprehensive model of geothermal exploitation for depleted deep heavy oil reservoirs through supercritical CO₂ injection.
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In a deal described as possibly "one of the most important transactions BP has done in 20 years" by CEO Murray Auchincloss, the company has agreed contractual terms with the Iraqi government to invest in drilling and infrastructure to rehabilitate and boost production at the Kirkuk oil field.
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Investigation into the parameters affecting the displacement efficiency of CO2 injection under miscible and near-miscible conditions in the presence and absence of mobile water saturation using carbon dioxide core injection experiments.
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CO₂ enhanced oil recovery (EOR) provides an attractive and commercially established technique to store CO₂ underground. EOR modeling is crucial because complex simulation is required to predict the behavior of CO₂ and its interaction with the oil and reservoir rock.
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The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.
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The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.
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The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
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This polymer retention study examines temperature effect on an ATBS-based polymer, using single- and two-phase retention studies and different analytical methods in the presence and absence of oil.
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The partnership between GLJ and Energy Fuse Group aims to leverage the expertise of both companies in project management, operational design, and subsurface and commercial evaluations to facilitate effective CO2 storage solutions.
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