2023
DOI: 10.1007/s13202-023-01617-2
|View full text |Cite
|
Sign up to set email alerts
|

Estimating electrical resistivity from logging data for oil wells using machine learning

Abstract: Formation resistivity is crucial for calculating water saturation, which, in turn, is used to estimate the stock-tank oil initially in place. However, obtaining a complete resistivity log can be challenging due to high costs, equipment failure, or data loss. To overcome this issue, this study introduces novel machine learning models that can be used to predict the electrical resistivity of oil wells, using conventional well logs. The analysis utilized gamma-ray (GR), delta time compressional logs (DTC), sonic … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 31 publications
0
1
0
Order By: Relevance
“…It takes the average of results from multiple DTR to arrive at the final forecast. The prediction outcome is determined by computing the mean of the outputs generated by each tree [28]. It was first introduced in 2001 by University of California, Berkeley professor Leo Breiman.…”
Section: Random Forest Regressor (Rfr)mentioning
confidence: 99%
“…It takes the average of results from multiple DTR to arrive at the final forecast. The prediction outcome is determined by computing the mean of the outputs generated by each tree [28]. It was first introduced in 2001 by University of California, Berkeley professor Leo Breiman.…”
Section: Random Forest Regressor (Rfr)mentioning
confidence: 99%
“…In practice, the determination of correlations between resistivity and seismic wave velocity is possible for genetically defined, relatively homogenous rock formations but, unfortunately, it often is rather ambiguous. Modern statistical methods are quite helpful [4] together with machine learning methodologies [10][11][12][13]. In the project reported below, the authors applied the modern method of integrated inversion of independent geophysical data, which is still under development.…”
Section: Introductionmentioning
confidence: 99%