CO2 EOR

The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.

The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

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.

In this paper, the authors propose polymer-assisted water-alternating-gas (WAG) injection as an alternative method to reduce gas mobility while reducing the mobility of the aqueous phase and, consequently, improving WAG performance.

The authors present an efficient microfluidic platform to measure high-quality minimum miscibility pressure data of CO2 with various impurities faster and easier.

ExxonMobil’s Liza Phase 1 and Phase 2 project and ADNOC’s CCUS Evolution Journey were the winners at the conference taking place this week in Dhahran.

Chevron has some new ideas on how to get more oil for less cost out of shale wells in their early years.

In the US, localized opposition and regulatory uncertainty are threatening to kill or severely limit the use of carbon capture, use, and storage (CCUS) in the fight against climate change.

In this paper, the authors evaluate the simultaneous optimization of CO2 storage and oil recovery using multiple injection strategies.

The $1-billion carbon capture project in Texas is revived after a 3-year hiatus.