Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu, Yi; Zhu, Runhua; Zhai, Shuo; Li, Na; Li, Chengyong

doi:10.1002/ese3.1579

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Consequently, a machine learning-based prediction for buried hill reservoir development patterns was established. To enhance the accuracy assessment of the model, it is essential to establish a comprehensive set of evaluation indices [39][40][41]. Traditionally, prediction accuracy has been used to measure model performance; however, in cases where sample sizes are imbalanced, accuracy alone fails to reflect the true predictive capability of the model.…”

Section: Model Establishment and Evaluationmentioning

confidence: 99%

Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application

Wang,

Mao,

Yang

et al. 2024

Processes

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In order to further understand the complex spatial distribution caused by the extremely strong heterogeneity of buried hill reservoirs, this paper proposes a new method for predicting the development pattern of buried hill reservoirs based on the traditional pre-drilling prediction and post-drilling evaluation methods that mainly rely on seismic, logging, and core data, which are difficult to meet the timeliness and accuracy of drilling operations. Firstly, the box method and normalization formula are used to process and normalize the abnormal data of element logging and engineering logging, and then the stepwise regression analysis method is used to optimize the sensitive parameters of element logging and engineering logging. The Light Gradient Boosting Machine (LightGBM) algorithm, deep neural network (DNN), and support vector machine (SVM) are used to establish a new method for predicting the development pattern of buried hill reservoirs. Lastly, a comprehensive evaluation index F1 score for the model is established to evaluate the prediction model for the development pattern of buried hill reservoirs. The F1 score value obtained from this model’s comprehensive evaluation index indicates that the LightGBM model achieves the highest accuracy, with 96.7% accuracy in identifying weathered zones and 95.8% accuracy in identifying interior zones. The practical application demonstrates that this method can rapidly and accurately predict the development mode of buried hill reservoirs while providing a new approach for efficient on-site exploration and decision-making in oil and gas field developments. Consequently, it effectively promotes exploration activities as well as enhances the overall process of oil and gas reservoir exploration.

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Section: Model Establishment and Evaluationmentioning

confidence: 99%

Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application

Wang,

Mao,

Yang

et al. 2024

Processes

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Machine learning-based grayscale analyses for lithofacies identification of the Shahejie formation, Bohai Bay Basin, China

Wang,

Xu,

Hao

et al. 2024

Petroleum Science

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Prediction of Total Organic Carbon Content in Deep Marine Shale Reservoirs Based on a Super Hybrid Machine Learning Model

Liu,

Li,

et al. 2024

Energy Fuels

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The total organic carbon (TOC) content is crucial for assessing the gas-bearing potential of shale reservoirs. Thus, quantitative characterization and intelligent prediction of TOC content play important roles in determining geological sweet spots and the development of shale reservoirs. Unfortunately, directly obtaining TOC content data in deep marine shale reservoirs is difficult, and the accuracy of indirect prediction remains insufficient. To efficiently and accurately predict TOC content, we propose a super hybrid prediction model, CVMD-CNN-BiLSTM-AT, which integrates correlation variational modal decomposition (CVMD), a convolutional neural network (CNN), a bidirectional long shortterm memory network (BiLSTM), and an attention mechanism (AT). The model employs CVMD to remove noise signals from the original TOC sequence, decomposes the denoised sequence into stable subsequence components, and a CNN-BiLSTM prediction model is constructed for each one. In addition, we incorporate AT to assign the hidden layer probability weights of BiLSTM, which makes the model focus on high-importance features and assigns weights accordingly. Finally, the predicted subsequences are combined and reconstructed according to the decomposition law to obtain the final TOC content. Herein, 1007 core samples and their related well logging data were collected from 13 typical wells, among which data from 705 samples were utilized to train the model and the remaining data were utilized to validate and test the model. The study results indicate that the CVMD-CNN-BiLSTM-AT model has excellent and reliable predictive ability, with an R 2 of 0.967 and can accurately predict TOC content. This achievement can provide adequate technical support and insight for deep marine shale gas exploration and development.

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Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Cited by 6 publications

References 27 publications

Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application

Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application

Machine learning-based grayscale analyses for lithofacies identification of the Shahejie formation, Bohai Bay Basin, China

Prediction of Total Organic Carbon Content in Deep Marine Shale Reservoirs Based on a Super Hybrid Machine Learning Model

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