permeability

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.

The F field in the Middle East currently has more than 40 producing wells in the center of the structure. The uneven well distribution limits the understanding of 3D reservoir characterization, particularly in the flank areas.

A US oil company is using its in-house laboratory's digital imaging devices to analyze reservoir rocks in new plays to answer questions raised by complex tight oil formations that cannot be answered using routine core testing methods.