A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs

Valentin, Manuel Blanco; Bom, Clécio R.; Coelho, Juliana; Correia, Maury Duarte; Albuquerque, Márcio P. de; Albuquerque, Marcelo P. de; Faria, Elisângela L.

doi:10.1016/j.petrol.2019.04.030

Cited by 67 publications

(22 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xingyi Xu reports on the application of machine learning (ML) methods to extract longitudinal phase information on parameters of the synchrotron damping oscillation [15]. Valentín et al proposes a novel methodology for automatic detection in borehole acoustic image logs of such structures using a single fast region-based convolutional neural network (fast-RCNN) [16,17].…”

Section: Related Workmentioning

confidence: 99%

A Rapid Method for Information Extraction from Borehole Log Images

Zhang

Tian

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Borehole logs are very important for geological analysis and application. Extracting structured information from borehole logs in the image format is the key to any analysis and application based on borehole data. The current method has defects in solving the beard phenomenon of the borehole log and the identification of special geological symbols. This paper proposes an automatic extraction method for borehole log information by combining the structural analysis based on the corner mark, as well as the structural understanding based on deep learning. The principles and key technologies of the method are described in detail. The performance of the method was tested by specific examples. This method is implemented on a geological information platform called QuantyView. The information extraction of 100 borehole logs with the same specification is used to verify the effectiveness of the proposed method. The results show that the method can not only effectively solve the inconsistency between the thickness and the description information in the borehole log but it can also address the low recognition efficiency of professional vocabulary, which can improve the extraction efficiency and accuracy of the borehole log information.

show abstract

Section: Related Workmentioning

confidence: 99%

A Rapid Method for Information Extraction from Borehole Log Images

Zhang

Tian

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Xie et al [22] compared the cross-validation accuracies of different machine learning models to determine the most appropriate machine learning method. Valentín et al [23] used wellbore image logs to identify lithologicalfacies by deep residual convolutional neural network. In brief, applications of machine learning models to perform reservoir classification based on logging response have been widely reported and has been shown to be capable of obtaining reliable prediction results [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Bi-LSTM Deep Neural Network Reservoir Classification Model Based on the Innovative Input of Logging Curve Response Sequences

Zhou

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

Reservoir classification is an important component of reservoir geological modelling and reservoir evaluation and identification. Using a single conventional logging curve to identify complex heterogeneous reservoir types has always been a difficult task in logging interpretation. For the first time, this study reveals the advantages of recurrent neural networks in the identification of heterogeneous reservoirs and proposes an optimal parameter bidirectional long short-term memory (Bi-LSTM) recurrent neural network reservoir classification model with optimal parameters that can make full use of logging sequence information. The data used in this work originate from 3 wells in the BZ gas field in China. First, the rationality of the data set and the generation of sequence data were studied in detail, and the logging curve response sequence data, which can fully characterize the reservoir characteristics, were obtained. Then, through multiple simulation experiments, the optimal network structure and hyperparameters were determined, and a Bi-LSTM network model with 5 hidden layers and the optimal network parameters was established. The model was used to predict fractured, pore-fracture and fracture-pore reservoirs in the buried hill metamorphic rock buried beneath the BZ gas field. A comparison with the prediction results of 5 classic machine learning methods and baseline models shows that the Bi-LSTM model with the optimal parameters is superior to the other machine learning methods, especially regarding the prediction accuracy of pore-fracture reservoirs, and the overall accuracy is 92.69%. The method proposed in this paper can accurately identify the strata developed in different types of storage space and significantly improves the reservoir identification accuracy. INDEX TERMS Bi-LSTM, logging curve response sequences, deep neural network, reservoir classification.

show abstract

“…In another publication De Lima et al [14] explored the use of deep convolutional networks to accelerate the microfacies classification based on rock thin sections. Valentin et al [50] introduced a methodology for automatic lithofacies identification based on ultrasonic and microresistivity borehole images and a deep residual convolutional network. Baraboshkin et al [6] compared the performance of several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) to classify rock types based on the optical core images.…”

Section: Introductionmentioning

confidence: 99%

Lithology classification of whole core CT scans using convolutional neural networks

Chawshin

Berg

Varagnolo³

et al. 2021

SN Appl. Sci.

View full text Add to dashboard Cite

X-ray computerized tomography (CT) images as digital representations of whole cores can provide valuable information on the composition and internal structure of cores extracted from wells. Incorporation of millimeter-scale core CT data into lithology classification workflows can result in high-resolution lithology description. In this study, we use 2D core CT scan image slices to train a convolutional neural network (CNN) whose purpose is to automatically predict the lithology of a well on the Norwegian continental shelf. The images are preprocessed prior to training, i.e., undesired artefacts are automatically flagged and removed from further analysis. The training data include expert-derived lithofacies classes obtained by manual core description. The trained classifier is used to predict lithofacies on a set of test images that are unseen by the classifier. The prediction results reveal that distinct classes are predicted with high recall (up to 92%). However, there are misclassification rates associated with similarities in gray-scale values and transport properties. To postprocess the acquired results, we identified and merged similar lithofacies classes through ad hoc analysis considering the degree of confusion from the prediction confusion matrix and aided by porosity–permeability cross-plot relationships. Based on this analysis, the lithofacies classes are merged into four rock classes. Another CNN classifier trained on the resulting rock classes generalize well, with higher pixel-wise precision when detecting thin layers and bed boundaries compared to the manual core description. Thus, the classifier provides additional and complementing information to the already existing rock type description. Article Highlights A workflow for automatic lithofacies classification using whole core 2D image slices and CNN is introduced. The proposed classifier shows lithology-dependent accuracies. The prediction confusion matrix is exploited as a tool to identify lithofacies classes with similar transport properties and to automatically generate lithofacies hierarchies.

show abstract

A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs

Cited by 67 publications

References 29 publications

A Rapid Method for Information Extraction from Borehole Log Images

A Rapid Method for Information Extraction from Borehole Log Images

Bi-LSTM Deep Neural Network Reservoir Classification Model Based on the Innovative Input of Logging Curve Response Sequences

Lithology classification of whole core CT scans using convolutional neural networks

Contact Info

Product

Resources

About