Unconventional/complex reservoirs
Conflict‑driven price gains may be offset by higher costs, supply‑chain risks, and a limited appetite for new drilling activity.
Examples demonstrate how an Integrated Operations Center as a Service (IOCaaS) model, powered by artificial intelligence, reduced costs by 5% and increased production by 6% in Canada.
This paper introduces a novel steam-sensitive flow-control device designed to restrict the production of steam and low-subcool liquids while allowing higher mobility of oil-phase fluids.
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This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir. -
One hydraulic fracturing job can stimulate two wells, but economic success hinges on doing it in the right place for the right price.
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This paper presents a multidisciplinary view of the evolution of a development project for the central area of Sururu and the method applied to address challenges and propose solutions.
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This paper assesses the potential of augmented depletion development in four US plays: Bakken, Eagle Ford, Midland, and the Anadarko Basin.
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Rig counts are down since 2023, but well productivity is marching forward.
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The deal comes only weeks after the private equity firm purchased a natural gas-fired plant operator.
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This paper describes a data-driven approach for liquid-loading detection and prediction that harnesses high-frequency gas-rate and tubinghead-pressure measurements to identify the onset of liquid loading and correct critical rates computed by empirical methods.
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This paper outlines the importance of numerical rate transient analysis for dry gas wells, describing a simple, fully penetrating planar fracture model.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
