petrophysics

This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.

The authors of this paper present a workflow designed to achieve maximum integration between analytical and modeling activities in carbon capture and storage projects.

This paper presents a complete digital workflow applied to several greenfields in the Asia Pacific region that leads to successful deep-transient-testing operations initiated from intelligent planning that positively affected field-development decisions.

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 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.

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.

This paper provides a work flow based on research in two methodologies to estimate formation-water salinity, enhancing the quality of saturation evaluation for quick decision-making during logging operations.

The authors write that simple and straightforward observations on outcrops can be used to build 3D models that mimic geological relationships accurately.

This paper presents an integrated work flow to model mechanical properties at sufficiently high resolution to honor accurately rock fabric and its effects on height and complexity and, thus, production.