SPE Journal

A novel approach uses the heart-shaped signal in low-frequency distributed acoustic sensing measurements to estimate the hydraulic fracture tip distance before the hydraulic fracture intersects the monitor well, offering critical insight into the characterization of hydraulic fracture propagation.

This investigation of fluid flow and particles movement uses a coupled CFD/DEM modeling approach to provide new insights into the multiscale mechanism of sand production.

Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.

This case study uses distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope, highlighting the versatile potential of DTS in long-term monitoring and establishing a workflow that makes the most of that potential.

This paper presents a comprehensive literature review and critical examination of the published modeling and experimental studies regarding the recovery mechanisms of cyclic gas injection and the conditions under which the process can enhance oil recovery with the aim to identify lessons learned and areas in need of further study.

This case study investigates the reasoning behind the solidification of barite behind the casing with the aim of developing solutions for efficient casing removal and the potential use of settled barite as a barrier material.

A fundamental research study is conducted to confirm the potential usefulness of chelating agents in dispersing settled barite, emphasize the complex nature of the factors that control this process, and provide a solid basis for future studies aimed at optimizing dispersion methods in industrial applications.

This paper proposes a holistic, automatic, and real-time characterization of cuttings/cavings, including their volume, size distribution, and shape/morphology, while integrating 3D data with high-resolution images to pursue this objective for use in the real-time assessment of hole cleaning sufficiency and wellbore stability and, consequently, for the prediction, prev…

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 clear and consistent method for determining dead and live crude EACNs using a single reliable method, highlighting a graphical way to determine the optimal salinity and its uncertainties using real data.