ArticleACS omega2024
Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A physics-informed hybrid ML framework for pore pressure and fracture gradient prediction in carbonate reservoirs.Scientific reports · 2026Article
- Enhancing wellbore stability through machine learning for sustainable hydrocarbon exploitation.Scientific reports · 2025Article
- Automated oil spill detection using deep learning and SAR satellite data for the northern entrance of the Suez Canal.Scientific reports · 2025Article
- Porosity prediction of tight reservoir rock using well logging data and machine learning.Scientific reports · 2025Article
- Machine learning-based estimation of crude oil-nitrogen interfacial tension.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.