The carbon dioxide (CO2) based enhanced oil recovery methods (EORs) are considered
among the promising techniques for increasing the recovery factor from mature oil reservoirs …

This study provides the proof-of-concept for identifying the most suitable machine-learning
(ML) model that predicts minimum miscibility pressure (MMP) based on temperature, crude …

This paper focuses on the current application of machine learning (ML) in enhanced oil
recovery (EOR) through CO2 injection, which exhibits promising economic and …

This study presents utilization of multiple data-driven models for predicting CO 2 minimum
miscibility pressure (MMP). The aim is to address the issue of existing models lacking …

The accurate prediction of underground formation lithology class and tops is a critical
challenge in the oil industry. This paper presents a machine-learning (ML) approach to …

The minimum miscibility pressure (MMP) is an important reference parameter in the study of
CO2 oil drive systems. In response to the problems of time‐consuming and costly prediction …

Confronting the inefficiencies in current drilling industry practices, particularly the limitations
of traditional scalar value-based assessments. This study introduces a novel machine …

Accurately forecasting oil and gas well production, especially in complex unconventional
reservoirs, is vital. Leveraging advanced techniques like machine learning and deep …

Estimating Shale Volume using conventional methods in the Bakken formation, a complex
heterogeneous reservoir, is challenging due to the presence of other radioactive minerals …

Quantifying mineral volumes is crucial for accurate characterization of complex and
unconventional reservoirs. Multimineral analysis is a technique used to estimate the …