carbonates

This paper extends an integrated two-scale continuum model that contemplates mass, momentum, and energy changes to study the acid-stimulation process in complex carbonate acid-stimulation systems with the development of fracture and vug networks.

The authors of this paper write that autonomous outflow-control devices can positively affect matrix acidizing by providing the best possible conformance.

This study combines preshear degradation, permeability, and oil presence effects to evaluate and improve polymer injectivity using acrylamido tertiary butyl sulfonate (ATBS) polymer in carbonate rock.

The main goal of this research work was to determine subseismic faults and fracture corridors and their characteristics, including density and orientation, for a Paleocene fractured carbonate reservoir.

This paper describes a collaborative project to analyze affected wells, identify commonalities, and optimize bit design and drilling parameters to mitigate the effects of borehole spiraling.

This paper discusses the approach used to sectorize a mature giant carbonate reservoir located onshore Abu Dhabi for the purposes of reservoir management, offtake, and injection balancing.

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.

This paper presents a unique investigation into determining the sufficient concentration of hardness ions required to significantly reduce the adsorption of acrylamide-tertiary-butyl-sulfonate-based polymer with a focus on mitigating polymer retention in carbonate formations using softened brine.

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.

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