Automatic Identification of Sedimentary Facies Based on a Support Vector Machine in the Aryskum Graben, Kazakhstan

The Aryskum Depression in the South Turgay Basin has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research. Among them, lithologic stratigraphic reservoirs have gradually become one of the focuses of oil and gas exploration. At present, deduction of the sedimentary characteristics of the target layer through core wells using artificial exploration has become an urgent problem to be solved. We selected 16 artificially interpreted coring wells in the Aryskum Graben for this study. Using the parameters of the gamma-ray (GR) curve of coring wells and support vector machine (SVM) classification algorithms, we developed an automatic identification model of sedimentary facies in the study area. The application of the SVM includes the following steps: Firstly, using the GR curve of 16 coring wells, six quantitative indexes defined as standard deviation, relative gravity, curve amplitude ratio, average median, average slope, and mutation amplitude, are selected to quantify the logging curve in the study area, thus realizing the description of the logging curve form. Secondly, training samples are selected to establish an SVM classification model. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional facies. Field application shows that this solution can be effectively used in uncored wells to identify depositional facies with a rate of accuracy approaching 70%. Our results provide new methods for the identification of sedimentary facies in the study area. The results will also provide a theoretical basis, as well as data basis, for further fine division of microfacies in the study area.

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Study about Recognition of Digital Meter Dial Reading Based on SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.615.194 ◽

2014 ◽

Vol 615 ◽

pp. 194-197

Author(s):

Zhen Yuan Tu ◽

Fang Hua Ning ◽

Wu Jia Yu

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Recognition Rate ◽

Classification Model ◽

Support Vector ◽

Feature Points ◽

Svm Classification ◽

Performance Factors ◽

Model Combining ◽

Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

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Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

RSC Advances ◽

10.1039/c5ra05663b ◽

2015 ◽

Vol 5 (61) ◽

pp. 49195-49203 ◽

Cited By ~ 9

Author(s):

Ting-Ting Yao ◽

Jing-Li Cheng ◽

Bing-Rong Xu ◽

Min-Zhe Zhang ◽

Yong-Zhou Hu ◽

...

Keyword(s):

Support Vector Machine ◽

Rational Design ◽

Classification Model ◽

Support Vector ◽

Svm Classification ◽

Tetronic Acid ◽

Model Based

A novel SVM classification model was constructed and applied in the development of novel tetronic acid derivatives as potent insecticidal and acaricidal agents.

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A pattern recognition modeling approach based on the intelligent ensemble classifier: Application to identification and appraisal of water-flooded layers

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651818803725 ◽

2018 ◽

Vol 233 (7) ◽

pp. 737-750

Author(s):

Zhiqiang Geng ◽

Xuan Hu ◽

Ning Ding ◽

Shanshan Zhao ◽

Yongming Han

Keyword(s):

Support Vector Machine ◽

Oil And Gas ◽

Classification Problem ◽

Training Sample ◽

Ensemble Classifier ◽

Bayesian Classifier ◽

Classification Model ◽

Support Vector ◽

Modeling Approach ◽

Model Free

Since the actual chromatogram data of the water-flooded layer have characteristics of multiple dimension, complexity and noise, it is difficult to accurately identify and appraise the water-flooded layer in the oil and gas reservoirs. Therefore, this article proposes a recognition modeling approach based on the intelligent ensemble classifier, integrated model-free Bayesian classifier, the AdaBoost algorithm and the support vector machine algorithm. The effective chromatogram characteristic information can be obtained using the curve fitting method. In order to transform the sparse classification problem into a general classification problem, the synthetic minority over-sampling technique algorithm is used to process an unbalanced training sample as a general training sample. Moreover, the model-free Bayesian classifier, AdaBoost and support vector machine algorithms are used as the base classifiers to train the ensemble classification model. Compared to the traditional single classification approach, the robustness and the effectiveness of the ensemble classifier model are validated through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is applied in the identification and appraisal of the water-flooded layers in a complex oil and gas recognition system. The chromatogram characteristic information and the prediction results are obtained to provide more reliable water-flooded layer information, guide the process of reservoir exploration and improve the oil development efficiency.

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

Sustainability ◽

10.3390/su11071919 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1919 ◽

Cited By ~ 5

Author(s):

Dahai Wang ◽

Jun Peng ◽

Qian Yu ◽

Yuanyuan Chen ◽

Hanghang Yu

Keyword(s):

Support Vector Machine ◽

Oil And Gas ◽

Field Application ◽

Well Logs ◽

Support Vector ◽

Gas Reservoirs ◽

Oil And Gas Exploration ◽

Core Samples ◽

Svm Algorithm ◽

The Cost

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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Study on the Interpretation Method of Low Resistivity Contrast Oil Reservoir Based on Support Vector Machine—Taking the Chang 8 Tight Sandstone Reservoir of Yanchang Formation in Huanxian Area, Ordos Basin as an Example

10.21203/rs.3.rs-985788/v1 ◽

2021 ◽

Author(s):

Ze Bai ◽

Maojin Tan ◽

Yujiang Shi ◽

Xingning Guan

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Ordos Basin ◽

Classification Model ◽

Support Vector ◽

Tight Sandstone ◽

Yanchang Formation ◽

Svm Classification ◽

Low Resistivity ◽

Sandstone Reservoir

Abstract Low resistivity contrast oil reservoirs are subtle reservoirs that have no obvious difference in physical and electrical properties from water layers. It is difficult to identify based on the characteristics of the geophysical well logging response. Especially in tight sandstone reservoirs with low porosity and low permeability, the log interpretation effect of low resistivity contrast oil layers is worse. In recent years, data mining technology has been increasingly applied in oil exploration and development, especially for some complex reservoirs with unclear logging response characteristics, and how to use data mining technology to effectively solve some complex problems is of great significance in oilfields. Therefore, support vector machine (SVM) technology was applied to interpret the low resistivity contrast oil layer in this paper. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, the two models were applied to the logging interpretation of the Chang 8 tight sandstone reservoir of the Yanchang Formation in the Huanxian area, Ordos Basin. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method and BP neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the low resistivity contrast oil layer by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

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Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

Current Bioinformatics ◽

10.2174/1574893615999200819162140 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chun Qiu ◽

Sai Li ◽

Shenghui Yang ◽

Lin Wang ◽

Aihui Zeng ◽

...

Keyword(s):

Support Vector Machine ◽

Expression Profiles ◽

Independent Set ◽

Classification Model ◽

Support Vector ◽

Feature Subset ◽

Hub Genes ◽

Effective Prevention ◽

Key Genes ◽

Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

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A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽

10.3390/sym13050757 ◽

2021 ◽

Vol 13 (5) ◽

pp. 757

Author(s):

Yongke Pan ◽

Kewen Xia ◽

Li Wang ◽

Ziping He

Keyword(s):

Optimization Algorithm ◽

Oil And Gas ◽

The Other ◽

Whale Optimization Algorithm ◽

Classification Model ◽

Support Vector ◽

Recognition Model ◽

Novel Approach ◽

Whale Optimization ◽

Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

Molecules ◽

10.3390/molecules17044560 ◽

2012 ◽

Vol 17 (4) ◽

pp. 4560-4582 ◽

Cited By ~ 2

Author(s):

Khac-Minh Thai ◽

Thuy-Quyen Nguyen ◽

Trieu-Du Ngo ◽

Thanh-Dao Tran ◽

Thi-Ngoc-Phuong Huynh

Keyword(s):

Support Vector Machine ◽

Inhibitory Activity ◽

Topoisomerase I ◽

Classification Model ◽

Support Vector ◽

Support Vector Machine Classification

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KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

Jurnal Informatika dan Rekayasa Elektronik ◽

10.36595/jire.v2i2.117 ◽

2019 ◽

Vol 2 (2) ◽

pp. 43

Author(s):

Lalu Mutawalli ◽

Mohammad Taufan Asri Zaen ◽

Wire Bagye

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Big Data ◽

Mass Communication ◽

Confusion Matrix ◽

Classification Model ◽

Support Vector ◽

Large Set ◽

Appearance Time ◽

Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

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Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.3499 ◽

2014 ◽

Vol 8 (1) ◽

pp. 91 ◽

Cited By ~ 5

Author(s):

Noviah Dwi Putranti ◽

Edi Winarko

Keyword(s):

Support Vector Machine ◽

Maximum Entropy ◽

Social Networking Site ◽

Training Data ◽

Classification Model ◽

Support Vector ◽

Public Sentiment ◽

Pos Tagger ◽

Negative Sentiment ◽

Bahasa Indonesia

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif. Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 % pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%. Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter. AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.

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