Predicting Vertical Resistivity By Machine Learning

Vereshagin, A.; Wedberg, Torolf C.; Stefatos, Aris

doi:10.3997/2214-4609.201901610

81st EAGE Conference and Exhibition 2019 2019

DOI: 10.3997/2214-4609.201901610

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Predicting Vertical Resistivity By Machine Learning

A. Vereshagin¹,

Torolf C. Wedberg²,

Aris Stefatos³

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

Al-Fakih

Ibrahim

Elkatatny

et al. 2023

J Petrol Explor Prod Technol

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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 shear log (DSTM), neutron porosity, and bulk density. The study utilized a dataset of 3529 logging data points from horizontal oil carbonate wells which were used to develop different machine learning models using random forest (RF) and decision tree (DT) algorithms. The obtained results showed that both models can predict electrical resistivity with high accuracy, over 0.94 for training and testing data. Comparing the models based on accuracy and consistency revealed that the RF model had a slight advantage over the DT model. Based on the data analysis, it was found that the formation resistivity is more significantly impacted by GR logs compared to DTC logs. These new ML models offer a low-cost and practical alternative to estimate well resistivity in oil wells, providing valuable information for geophysical and geological interpretation.

show abstract

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

Al-Fakih

Ibrahim

Elkatatny

et al. 2023

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

Deep learning for low-frequency extrapolation from multioffset seismic data

et al. 2019

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Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

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MR3D phase 4: the synthetic resistivity well log dataset

Menezes,

Menor

2024

Front. Earth Sci.

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Predicting Vertical Resistivity By Machine Learning

Cited by 3 publications

References 0 publications

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

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

Deep learning for low-frequency extrapolation from multioffset seismic data

MR3D phase 4: the synthetic resistivity well log dataset

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