Texas A&M University

This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.

This paper presents a novel methodology for assessing the rapid mineral carbonation of carbon dioxide through geochemical interactions with carbon-, magnesium-, and iron-rich minerals abundant in geological formations.

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 an adaptive grid-coarsening approach based on constraints that honor reservoir structure and stratigraphy, preserve fluid volumes and contacts, and retain resolution near wells.

This work proposes a method to interpret far-field strain-change and pressure data to quantify fracture connectivity and properties at the cluster level.

The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models.

The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.

This paper assesses the potential of augmented depletion development in four US plays: Bakken, Eagle Ford, Midland, and the Anadarko Basin.

Former SPE President Tom Blasingame began his term as head of Texas A&M's petroleum engineering department on 1 May.

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.