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

Wang, Dahai; Peng, Jun; Qiao, Yuhui; Chen, Yuanyuan; Yu, Hanghang

doi:10.3390/su11071919

Cited by 19 publications

(9 citation statements)

References 38 publications

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“…However, it should be noted that to realize the multi-classification task, the method of "one against one" was used. Any two sample sequences were combined to construct C 2 N vector machines, the "vote" was adopted to classify and perform the identification of N types of depositional subfacies in the intervals [11]. It means that though this method is good, when there are N categories, the number of models is N(N−1) 2 .…”

Section: Discussionmentioning

confidence: 99%

“…When RM < 0.5, the curve is funnel-shaped, and the center of gravity is higher. When RM ≈ 0.5, the curve shape is a box type [11].…”

Section: Prepare the Logging Datamentioning

confidence: 99%

“…In recent years, machine learning has been preliminarily applied in processing logging data and predicting reservoir pore and permeability properties.International geologists are currently conducting research on the identification of sedimentary facies by machine learning. This mainly includes the KNN (k-nearest neighbor) [1], ANN (artificial neural network) [2][3][4][5], Bayesian [6], fuzzy clustering [7,8], and support vector machine (SVM) algorithms [9][10][11]. However, some of these machine learning algorithms have limitations and, in many cases, may not meet the requirements for the identification of sedimentary facies [12,13].…”

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confidence: 99%

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

Wang

Sun

2019

Applied Sciences

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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. Appl. Sci. 2019, 9, 4489 2 of 15 research, and lithologic stratigraphic reservoirs have gradually become some of the main focuses of oil and gas exploration.The determination of sedimentary facies plays an important role in the exploration of lithologic reservoirs, especially in the prediction of residual oil production. In addition, coring and logging are seldom carried out in development wells. Therefore, increasing attention has been paid to the method of studying sedimentary facies by logging curves. However, the process of manual identification often incurs many problems, such as an intense workload of data statistics and mapping, low work efficiency, poor quantification, and non-uniformity of the criteria for manual identification of sedimentary facies in uncored wells. These problems highlight that there are many shortcomings in the artificial identification of multi-well and multi-layer logging sedimentary facies. With the popularization of digital logging technology, digital logging curve technology, and computer technology, the machine learning method in particular has the advantages of dealing with non-linear mapping relations. In recent years, machine learning has been preliminarily applied in processing logging data and predicting reservoir pore and pe...

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Section: Discussionmentioning

confidence: 99%

“…When RM < 0.5, the curve is funnel-shaped, and the center of gravity is higher. When RM ≈ 0.5, the curve shape is a box type [11].…”

Section: Prepare the Logging Datamentioning

confidence: 99%

mentioning

confidence: 99%

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

Wang

Sun

2019

Applied Sciences

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“…During the last decade, improved computing hardware and software has led to the prosperous application of machine learning (ML) in different areas of oil industry such as seismic data, petrophysical analysis including synthetic log generation or prediction [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], which has shown to be a promising tool to help address their problems in a rigorous, repeatable way. Such methods, by considering various available parameters, can give a better prediction of the missing data than simple linear methods [10,26,27].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

Khatibi

Aghajanpour

2020

Energies

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For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports.

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“…These techniques have become some of the favorable computational methods for predicting the increase in sea level because they can achieve fast computation using only a few parameters as the input [17][18][19][20]. Support vector machines (SVMs) have recently attracted the interest of many researchers for different prediction scenarios [21,22]. Asefa et al [23] successfully used the SVM for the prediction of the Sevier River Basin (South-Central Utah, USA) at hourly and seasonal intervals.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

et al. 2019

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The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis.

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

Cited by 19 publications

References 38 publications

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

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

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

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