Unconventional/complex reservoirs
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.
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. -
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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The KRI has become a significant player in the oil and gas industry. The authors use production data from key fields to explore the factors influencing both individual fields and overall production. An overview of the challenges and milestones in the region’s oil and gas sector from 2014 to 2023 enhances understanding of its evolution, current status, and future oppor…
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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.
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Based on 6 years of firsthand experience, refrac experts share some of their biggest insights into where the US market is headed and how to identify the best candidates.
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The new frontier of production improvement combines surveillance techniques and analysis to determine which variables boost output.
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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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