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Identification of Porosity and Permeability While Drilling Based on Machine Learning

  • Research Article-Petroleum Engineering
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Abstract

The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model processing and calculation times as new logging data are incorporated while drilling. In this paper, four machine learning algorithms were evaluated: the one-versus-rest support vector machine (OVR SVM), one-versus-one support vector machine (OVO SVM), random forest (RF) and gradient boosting decision tree (GBDT) algorithms. First, samples of wireline logging data from the Yan969 wellblock of the Yan’an gas field were chosen for model training. To improve the accuracy and reduce the input parameter dimensions and model training time as much as possible, data correlation analysis was performed. Second, we used the grid search method to approximate ranges of reasonable parameter values and then used k-fold cross-validation to optimize the final parameters and avoid overfitting. Third, we used the four classification models to predict porosity and permeability while drilling with data from logging while drilling (LWD) logs. Finally, we indicate the best porosity and permeability prediction models to use while drilling. To ensure that the prediction accuracy is as high as possible and that the model training time is as short as possible, the OVO SVM algorithm was suggested for porosity and permeability prediction. Therefore, appropriate machine learning algorithms can be used to predict porosity and permeability while drilling.

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Acknowledgement

This work was supported by the (1) National Natural Science Foundation of China (NSFC) (No. 51974248 and No. 51704235), (2) Open Fund (PLC20190702) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology), (3) Open Fund of Shanxi Key Laboratory of Carbon Dioxide Storage and Enhanced Oil Recovery (YJSYZX20SKF0008), (4) Natural Science Basic Research Plan in Shanxi Province of China (2019JQ-407).

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Author notes

  1. Jian Sun and Rongjun Zhang contributed equally to this manuscript.

Authors and Affiliations

  1. College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, 710065, China

    Jian Sun, Rongjun Zhang, Mingqiang Chen, Qi Li & Long Ren

  2. Shanxi Key Laboratory of Carbon Dioxide Storage and Enhanced Oil Recovery, Xi’an, 710065, China

    Rongjun Zhang

  3. Xi’an Key Laboratory of Tight Oil (Shale Oil) Development (Xi’an Shiyou University), Xi’an, 710065, China

    Rongjun Zhang

  4. The 6Th Oil Production Plant of PetroChina Changqing Oilfield Company, Yulin, 718606, China

    Bo Chen

  5. Two Sigma Investments, LP, NY City, 10013, USA

    Xiao Wang

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  1. Jian Sun
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Correspondence to Jian Sun.

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Sun, J., Zhang, R., Chen, M. et al. Identification of Porosity and Permeability While Drilling Based on Machine Learning. Arab J Sci Eng 46, 7031–7045 (2021). https://doi.org/10.1007/s13369-021-05432-x

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