DSDE: In Theory

This paper presents an approach for automatic daily-drilling-report classification that incorporates new techniques of artificial intelligence.

The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.

The authors of this paper investigate the application of two seismic monitoring methods in monitoring carbon leaks: full waveform inversion and reverse-time migration.

The main objective of this paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions to better estimate conductive fractures and pressure profiles.

This paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.

The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.

The authors of this paper present an advanced dual-porosity, dual-permeability (A-DPDK) work flow that leverages benefits of discrete fracture and DPDK modeling approaches.

The authors of this paper present a machine-learning-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties.

This study presents a novel approach to screen thermally stable surfactants at high pressures and high temperatures for the explicit purpose of wettability alteration in the operator’s Eagle Ford acreage.

This paper presents a novel sustainable cost-effective method developed to scale up synthesis of Janus carbon nanoparticles from waste plastic feedstock by combined pyrolysis, chemical functionalization, and pulverization.