Performance of the synergetic wavelet transform and modified K-means clustering in lithology classification using nuclear log

Yang, Huaijie; Pan, Heping; Ma, Huolin; Konaté, Ahmed Amara

doi:10.1016/j.petrol.2016.02.031

Cited by 45 publications

(14 citation statements)

References 19 publications

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“…Clustering is subordinate to unsupervised learning, which does not rely on the defined classes and training examples of class labels. Among them, K-means clustering is a very classic clustering method [23].…”

Section: Methodsmentioning

confidence: 99%

Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Zhu

Qin³

et al. 2021

Geofluids

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The accurate calculation of the gas content of coalbed bed methane (CBM) reservoirs is of great significance. However, due to the weak correlation between the logging response of coalbed methane reservoirs and the gas content parameters and strong nonlinear characteristics, it is difficult for conventional gas content calculation algorithms to obtain more reliable results. This paper proposes a CBM reservoir gas content assessment method combining K-means clustering and random forest. The K-means clustering is used to divide the reservoirs and distinguish the types to establish a random forest model. Judging from the evaluation effect of the research block, the prediction accuracy of the new method is significantly higher than that of the original method, and more accurate gas content prediction values can be obtained for different types of reservoirs. Studies have shown that this method can help the gas content evaluation of CBM reservoirs, improve the accuracy of gas content evaluation, and better support the exploration and development of CBM reservoirs. The results of this study show that the random forest method based on clustering can effectively distinguish the relationship between different logging responses and gas content. On this basis, the random forest algorithm modeling can effectively characterize the complex relationship between gas content and logging curve response. In the case of poor correlation between gas content and logging curve, the gas content of the reservoir can also be accurately calculated.

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Section: Methodsmentioning

confidence: 99%

Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Zhu

Qin³

et al. 2021

Geofluids

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“…Appropriate regimentation of geochemical indicator elements of mineralization occurrences is a challenging issue due to the complexity of geological features, especially for a big geochemical data collection of stream sediments on a regional scale (Ghezelbash et al, 2019;Ghezelbash et al, 2020). Dividing geochemical indicator elements associated with mineral deposits into efficient and inefficient groups can be different due to employing various traditional clustering methods or factor analysis techniques (Templ et al, 2008;Yang et al, 2016). Thus, employment of a suitable methodology such as convolutional deep learning (CDL) algorithm can regiment big geochemical data into meaningful groups of indicator elements.…”

Section: Introductionmentioning

confidence: 99%

Regimentation of geochemical indicator elements employing convolutional deep learning algorithm

Sabbaghi

Tabatabaei²

2023

Front. Environ. Sci.

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Recently, deep learning algorithms have been popularly developed for identifying multi-element geochemical patterns related to various mineralization occurrences. Effective recognition of multi-element geochemical anomalies is essential for mineral exploration, and effective recognition is extremely dependent on integral clustering. Deep learning algorithms can achieve impressive results in comparison to the prior methods of clustering indicator elements correlated to mineralization for a region of interest due to their superb capability of extracting features from complex data. Although numerous supervised and unsupervised deep learning algorithms have been executed for the recognition of geochemical anomalies, employing them for clustering geochemical indicator elements is rarely observed. In this research, a convolutional deep learning (CDL) algorithm was architected to recognize and regiment geochemical indicator elements in Takht-e Soleyman District, Iran. Various opinions and experiments were considered to reach optimum parameters of this architecture. Fortunately, the achieved root mean square error (RMSE) values were in the appropriate range (<20%) which display the predicted values of the dependent variables (Pb as a pioneer of the first group and Ag as a pioneer of the second group) through their independent variables that are so close to their actual values. Also, the great R2adj calculated (more than 90%) for the last stage of regimentation confirms impressive accuracy and performance of the convolutional deep learning algorithm for clustering geochemical indicator elements of the study area.

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“…Advanced statistical models have been introduced to automate the task of facies identification. These include methods such as non-parametric regression, factor analysis, principal component analysis, classification trees, clustering and techniques based on machine learning and artificial intelligence [9][10][11][12]. The electrofacies and the lithofacies are similar in attempting to identify and group rocks based on large-scale geologic and petrophysical features as shown by the log responses.…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Puskarczyk

2020

Energies

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The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

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Performance of the synergetic wavelet transform and modified K-means clustering in lithology classification using nuclear log

Cited by 45 publications

References 19 publications

Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Regimentation of geochemical indicator elements employing convolutional deep learning algorithm

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

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