Lithofacies identification in carbonate reservoirs by multiple kernel Fisher discriminant analysis using conventional well logs: A case study in A oilfield, Zagros Basin, Iraq

Dong, Shaoqun; Zeng, Lianbo; Du, Xiangyi; He, Juan; Sun, Futing

doi:10.1016/j.petrol.2021.110081

Cited by 36 publications

(15 citation statements)

References 48 publications

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“…According to Formula (10), the rocks are categorized, and the category with the highest level of confidence is chosen as the output category for the rocks. The cross-entropy function is then utilized as the loss function, as shown in Formula (11) The particular expression is as follows: to improve the classification results, the less the cross entropy, the closer the network output is to the real value corresponding to the labeling results.…”

Section: Improvement Of Moboilenetv2 Rockiness Recognition Model Inco...mentioning

confidence: 99%

“…Lithology identification of rocks has been a hot topic of research in the fields of resource science, geological exploration, and geotechnical engineering at home and abroad [1,2]. Traditional and contemporary lithology identification techniques include naked eye identification [3], experimental analysis [4], thin section identification [5], and machine learning [6][7][8][9][10][11][12][13][14], among others. There are still flaws, despite the fact that the lithology identification of rocks has become more scientific and rational as a consequence of the investigations described above.…”

Section: Introductionmentioning

confidence: 99%

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Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

Zhang

Lei

et al. 2022

Applied Sciences

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How to achieve high-precision detection and real-time deployment of the lithology intelligent identification system has significant engineering implications in the geotechnical, geological, water conservation, and mining disciplines. In this study, a lightweight lithology intelligent identification model is proposed to overcome this problem. The MobileNetV2 model is utilized as the basic backbone network to decrease network operation parameters. Furthermore, channel attention and spatial attention methods are incorporated into the model to improve the network’s extraction of complicated and abstract petrographic elements. In addition, based on the findings of network training, computing power performance, test results, and Grad-CAM interpretability analysis and comparison tests with Resnet101, InceptionV3, and MobileNetV2 models. The training accuracy of the proposed model is 98.59 percent, the training duration is 76 min, and the trained model is just 6.38 megabytes in size. The precision (P), recall (R), and harmonic mean (FI-score) were, respectively, 89.62%, 91.38%, and 90.42%. Compared to the three competing models, the model presented in this work strikes a better balance between lithology recognition accuracy and speed, and it gives greater consideration to the rock feature area. Wider and more uniform, strong anti-interference capability, improved robustness and generalization performance of the model, which can be deployed in real-time on the client or edge devices and has some promotion value.

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Section: Improvement Of Moboilenetv2 Rockiness Recognition Model Inco...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

Zhang

Lei

et al. 2022

Applied Sciences

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show abstract

“…The improper selection of these two parameters can result in poor recognition performance and low accuracy. 15 Scholars have begun to focus on decision tree ensemble algorithms, which have strong interpretability, low data sample requirements, strong robustness, and suitability for large-scale data, to address the shortcomings of the single-model machine-learning methods mentioned above. Chen and Guestrin 16 elucidated the loss function of decision trees and created an XGBoost model to identify lithology.…”

Section: Introductionmentioning

confidence: 99%

“…However, the accuracy of the SVM in identifying rock types depends on the selection of the kernel function and penalty parameters. The improper selection of these two parameters can result in poor recognition performance and low accuracy 15 …”

Section: Introductionmentioning

confidence: 99%

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,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

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“…The motivation for this paper is derived from [27,28]. We considered similar issues by utilizing the KFDA method prior to proceeding with data classification.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

Tan,

Qin,

Tang

et al. 2023

Sensors

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Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model’s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

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Lithofacies identification in carbonate reservoirs by multiple kernel Fisher discriminant analysis using conventional well logs: A case study in A oilfield, Zagros Basin, Iraq

Cited by 36 publications

References 48 publications

Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

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

A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

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