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Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles

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The well logging technique is used to determine the petrophysical properties like porosity, permeability and fluid saturations of subsurface formations. However, the conventional way of log evaluation is very expensive and tiresome. The post-acquisition processing and inversion provides an alternative to determine the properties of drilled formations. This study proposes a novel approach to predict sonic log, adopting a regression method using a supervised machine learning (ML) algorithm, along with the determination of lithology employing clustering and a neural network approach grounded on the basis of gamma-ray log values and hence creating a correlation between the two. The scarce acoustic data obtained upon the traditional well logging procedure often pose a barrier in further determining the rock physics. Regression analysis, a predictive modeling technique, uses other petrophysical data to predict the sonic wave travel time (shear and compressional) by estimating a relationship between the two variables. The model is trained on a set of 10,000 points with 80% training points giving an 86.314% accuracy result. RMSE and R2 scores for training points and testing points came out to be 2.622 and 0.95, and 2.55 and 0.96, respectively, which helps in the validation of the model. Effective lithology determination is a crucial step of reservoir characterization. Traditional methods of core sample inspection and using well logs, however, cannot meet the needs of real-time due to complex sediment environment and reservoir heterogeneity. To deal with the problem, an unsupervised ML model, K-means clustering, a method of vector quantization grouping unlabeled data into arbitrary clusters based on similarities with respect to distance from the center is used. The model gave the optimum number of clusters as 5 and showed a presence of siltstone, coal and sandstone separated between these clusters. The Silhouette score which tests the accuracy came out to be 0.5840 along with a CH score of 27,192.

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Source Norwegian Petroleum Directorate (https://www.npd.no/en/). Well 15/9-F-11 (T2) is used in the present study

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Acknowledgements

All the authors are gratefully acknowledged to the University of Petroleum & Energy Studies (UPES), Dehradun. The authors are thankful to Equinor for the availability of open-sourced data inventory (https://data.equinor.com) for research purposes. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Petroleum Engineering and Earth Sciences, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, Uttarakhand, India

    Dev Joshi, Atul Kumar Patidar, Abhipshit Mishra, Aditya Mishra, Somya Agarwal & Aayush Pandey

  2. Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, Uttarakhand, India

    Bhupesh Kumar Dewangan & Tanupriya Choudhury

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  1. Dev Joshi
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Contributions

DJ and AKP drew the objectives of the project and formulated the manuscript. DJ and AM drafted the methodology and lead to collect the data from open-sourced platforms. ADM, BKD and TPC lead to prepare the codes for data analysis. SA and AP assisted in the literature survey and draft preparation. AKP, BKD and TPC supervised the work and finally, all the authors synchronized the results, made interpretations and prepared this paper.

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Correspondence to Atul Kumar Patidar, Bhupesh Kumar Dewangan or Tanupriya Choudhury.

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Joshi, D., Patidar, A.K., Mishra, A. et al. Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles. GeoJournal 88 (Suppl 1), 47–68 (2023). https://doi.org/10.1007/s10708-021-10502-6

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