April 9, 2024SPE Western Regional Meeting

Data Analytics and Machine Learning Application for Reservoir Potential Prediction in Vuggy Carbonate Reservoirs Using Conventional Well Logging

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Machine learning models identify high-risk and low-risk carbonate reservoirs with high precision, overcoming limitations of missing specialized borehole image logs.
Accuracy rates exceed 85% when predicting reservoir potential in carbonate reservoirs, utilizing feature engineering across conventional well logging data.
Principal component analysis enables dimensionality reduction of well logs, supporting cost-effective evaluation of carbonate reservoirs without image logs.

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Implication

Computational framework demonstrates high-accuracy reservoir potential prediction in vuggy carbonate formations, highlighting a low-cost alternative to image logs.

Cite This Study

Gharieb et al. (2024) studied this question.

synapsesocial.com/papers/68e6fb9db6db6435876765cc https://doi.org/10.2118/218913-ms

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Implication

Computational framework demonstrates high-accuracy reservoir potential prediction in vuggy carbonate formations, highlighting a low-cost alternative to image logs.

Data Analytics and Machine Learning Application for Reservoir Potential Prediction in Vuggy Carbonate Reservoirs Using Conventional Well Logging

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Implication

Cite This Study

Data Analytics and Machine Learning Application for Reservoir Potential Prediction in Vuggy Carbonate Reservoirs Using Conventional Well Logging

Key Points

Discussion

Implication

Cite This Study

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