Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs

Sebtosheikh, Mohammad Ali; Motafakkerfard, Reza; Riahi, Mohammad Ali; Moradi, Shapour; Sabety, N.

doi:10.1007/s13146-014-0199-0

Cited by 45 publications

(10 citation statements)

References 24 publications

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“…Wang et al (2014) testified the application of SVM on the classification of organic-rich shale lithofacies, and optimistically indicated that SVM is very potential in providing reliable lithofacies outcomes. Sebtosheikh et al (2015) adopted SVM as a tool to execute a classification for lithofacies via well logs, and in accordance with the analysis of validating results eventually argued that SVM can be regarded as a great lithofacies prediction power for carbonate reservoirs. Although many practical cases have proved that those ML-based models are feasible in the lithofacies prediction, they are still not the best ones or the ideal predictors due to the following reasons:…”

Section: Introductionmentioning

confidence: 74%

“…Sebtosheikh et al. (2015) adopted SVM as a tool to execute a classification for lithofacies via well logs, and in accordance with the analysis of validating results eventually argued that SVM can be regarded as a great lithofacies prediction power for carbonate reservoirs. Although many practical cases have proved that those ML‐based models are feasible in the lithofacies prediction, they are still not the best ones or the ideal predictors due to the following reasons: (1) KNN or SVM will cause a time‐consuming phenomenon when employing a considerable amount of training samples because the former predicts each test data by all training samples and the latter applies a kernel matrix deduced from all training samples to finalize calculation; (2) KNN or PNN will quite possibly be ineffective when using a training dataset of which each cluster is unbalanced in volume, since under this circumstance the established decision boundary or the probability density distribution will be distorted and each test sample will then prefer to be categorized into the largest volumetric cluster; (3) sometimes a researcher hopes to generate an understandable classification rule from predictors, but the employment of PNN or SVM in this situation will be unpleasant as the predicting references produced by any of them are totally unexplainable.…”

Section: Introductionmentioning

confidence: 95%

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Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Zhang

Wang³

et al. 2022

Geophysical Prospecting

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Although lithofacies routinely is featured by distinct logging responses from each other, many types of lithofacies in practical cases show similar measuring characteristics on logs, and then to achieve a desirable solution from logging‐based lithofacies prediction actually is challengeable. Since the mathematical essence of lithofacies prediction can be explained as an issue of pattern recognition, a light gradient boosting machine, a state‐of‐the‐art ensemble learning, specifically developed to address supervised classification, could be a potential solver. Nonetheless, due to an incompatibility of inherent exclusive feature bundling algorithm for logs and usage of a great deal of hyper‐parameters, a raw light gradient boosting machine might not be suitable or functional to predict lithofacies. Thus, continuous restricted Boltzmann machine and Bayesian optimization, respectively, employed to process original logging data and optimize hyper‐parameters, are adopted as technical assistants for the light gradient boosting machine, and accordingly a new ensemble learning‐based predictor called continuous restricted Boltzmann machine – Bayesian optimization – the light gradient boosting machine, is proposed for lithofacies. To validate classifying capability and robust nature of new predictor, three experiments are designed purposefully based on the application of a dataset collected from the wells located within pre‐salt lacustrine carbonate reservoirs of the Santos Basin. Simultaneously, to highlight validating effect, other three sophisticated classifiers are introduced as competitors in all experiments, including supper vector machine, random forest and extreme gradient boosting. According to the analysis and comparison of all experimental results, overall four points are verified: (1) the integration of continuous restricted Boltzmann machine and Bayesian optimization indeed is beneficial for the light gradient boosting machine in data preprocess and modelling stage; (2) the light gradient boosting machine cored predictor shows more capability of producing reliable predicted lithofacies information compared to other three competitors; (3) training more learning samples is a simple and effective approach to enhance computing capability of any validated predictor, and under this circumstance the light gradient boosting machine cored predictor still performs relatively better; (4) the light gradient boosting machine cored predictor is characterized with a better robustness as its predicting outcomes, even derived from a sparse‐condition dataset, are also proved more qualified. Consequently, the new proposed predictor is demonstrated as a high‐efficient and robust solver for the prediction of lithofacies and deserves a widespread application in the geological, geophysical and petrophysical research.

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 95%

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Zhang

Wang³

et al. 2022

Geophysical Prospecting

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“…The performance of the ANN and FL methods are superior compared with statistical methods [6,7,12,13,19,28]. SOM methods provide better results in lithology classification compared to other machine learning techniques [29].…”

Section: Problem and The Backgroundmentioning

confidence: 97%

“…A number of methods have been used for solving lithology classification and interpretation problems, such as cross plot interpretation and statistical analysis based on histogram plotting [5], support vector machine (SVM) using traditional wireline well logs [6], fuzzy-logics (FL) for association analysis, neural networks and multivariable statistical methodologies [7], artificial intelligence approaches and multivariate statistical analysis [8], hybrid NN methods [9], self organized map (SOM) [10], FL methods [11], artificial neural network (ANN) methods [12,13], lithology classification from seismic tomography [14], multi-agent collaborative learning architecture approaches [15], random forest [16,17], generative adversarial network [18] and multivariate statistical methods [19].…”

Section: Problem and The Backgroundmentioning

confidence: 99%

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

et al. 2020

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Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.

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“…So far, the most used machine learning algorithms for the automatic identification of depositional microfacies by well logs include Bayesian, fuzzy clustering and artificial neural network (ANN) algorithms [11][12][13][14]. They have provided robust, efficient and effective platforms for solving problems [15].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

et al. 2019

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Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only the depositional microfacies in a few wells can be identified due to the limited core samples in these wells. In this study, the support vector machine (SVM) algorithm is proposed to identify depositional microfacies automatically using well logs. The application of SVM includes the following steps: First, the depositional microfacies are determined manually in several wells with core samples. Then, the training sets used in the SVM algorithm are extracted from the well logs. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional microfacies. Field application shows that this innovative and constructive solution can be effectively used in uncored wells to identify depositional microfacies with a rate of accuracy approaching 84%. It overcomes the limitation of the conventional manual method which greatly contributes to the cost-saving of core analysis and improves the sustainable profitability of oil and gas exploration.

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Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs

Cited by 45 publications

References 24 publications

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

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