Unconventional/complex reservoirs
As Africa’s top oil producer, Libya is ramping up momentum—offering 22 exploration areas and welcoming BP back to Tripoli with a major deal.
This study aims to thoroughly assess the influence of various completions, fracturing stimulation, and intrinsic reservoir properties affecting the productivity of 10 major unconventional plays while uncovering insights and trends unique to each play
This paper introduces new methods to perform reliable permeability and saturation-dependent relative permeability measurements in organic-rich mudrock core samples using a pressure-decay setup.
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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.
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This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.