Data-driven machine learning approach to predict mineralogy of organic-rich shales: An example from Qusaiba Shale, Rub'al Khali Basin, Saudi Arabia
Abstract The Qusaiba Shale is a proven source rock for the Palaeozoic petroleum system of
the middle east and it is targeted as a potential source of unconventional shale gas potential …
the middle east and it is targeted as a potential source of unconventional shale gas potential …
Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir
Carbonate rocks present a complicated pore system owing to the existence of intra-particle
and interparticle porosities. Therefore, characterization of carbonate rocks using …
and interparticle porosities. Therefore, characterization of carbonate rocks using …
Utilizing artificial intelligence techniques for modeling minimum miscibility pressure in carbon capture and utilization processes: a comprehensive review and …
MN Amar, H Djema, K Ourabah, FM Alqahtani… - Energy & …, 2024 - ACS Publications
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 …
among the promising techniques for increasing the recovery factor from mature oil reservoirs …
An intelligent approach to brittleness index estimation in gas shale reservoirs: A case study from a western Iranian basin
Brittleness index is one of the key parameters for characterization of unconventional shale
gas reservoirs and screening favorable hydraulic fracturing candidates. Brittleness index in …
gas reservoirs and screening favorable hydraulic fracturing candidates. Brittleness index in …
Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure
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 …
miscibility pressure (MMP). The aim is to address the issue of existing models lacking …
A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir
A Ayyaz Mustafa, Z Zeeshan Tariq… - International …, 2022 - onepetro.org
Carbonate rocks have a very complex pore system due to the presence of interparticle and
intra-particle porosities. This makes the acquisition and analysis of the petrophysical data …
intra-particle porosities. This makes the acquisition and analysis of the petrophysical data …
[PDF][PDF] A Machine Learning Approach for Stress Prediction in Granitoid Formation at FORGE Geothermal Site Using Compressional and Shear-wave Slowness
The accurate estimation of in-situ stresses is of vital importance for optimizing the subsurface
planning and design such as horizontal well placement, hydraulic fracturing, and wellbore …
planning and design such as horizontal well placement, hydraulic fracturing, and wellbore …
[PDF][PDF] IPTC-22081-MS
AA Mustafa, ZZ Tariq, MM Mahmoud… - parameters, 2022 - researchgate.net
Carbonate rocks have a very complex pore system due to the presence of interparticle and
intra-particle porosities. This makes the acquisition and analysis of the petrophysical data …
intra-particle porosities. This makes the acquisition and analysis of the petrophysical data …