permeability

This paper presents the application of degradable particulate-diverter technology to improve oil production by effectively diverting stimulation treatment fluids away from high-water-cut, high-permeability, high-temperature producing intervals or zones.

This paper describes modeling work performed to design fracturing treatment and spacing for wells in the Project Cape enhanced geothermal system in Utah.

This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.

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.

his paper investigates the challenges faced in the development of mature and tight fields, primarily resulting from reservoir depletion, high operational costs, and uncertainty in reserves volumetric calculations.

This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.

This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.

The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.

The author writes that the generally accepted Knudsen diffusion in shales is based on a mistranslation of the flow physics and may give theoretically unsound predictions of the increased permeability of shales to gas flow.

An extensive laboratory study was carried out with two objectives: to evaluate the effect of water quality on injectivity of disposal wells with reservoir core plugs and to restore injectivity of damaged wells.