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Kieu Duy Thong

Bio: Kieu Duy Thong is an academic researcher. The author has contributed to research in topics: Support vector machine & Supervised learning. The author has co-authored 1 publications.

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Journal ArticleDOI
18 Nov 2021-Energies
TL;DR: In this paper, the authors used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset, and then they applied supervised learning to predict HU by combining core and well log data.
Abstract: The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors proposed robust methods to identify the optimal hydraulic flow units (HFUs) using differential evolution (DE) and two-stage clustering, where the first stage uses OPTICS clustering algorithm to determine the number of HFUs and the second stage generates the desired clusters using the Gaussian mixture algorithm.
Abstract: One essential process in reservoir characterization is the identification of hydraulic flow units (HFUs). It plays a critical role in determining hydrocarbon reserves and improving reservoir productivity. Flow zone indicator (FZI), determined from core data, is widely used to identify HFUs. One of the challenges in the FZI technique is that the number of HFUs is identified using qualitative methods and subjective estimation. This work proposes robust methods to identify the optimal HFUs using differential evolution (DE) and two-stage clustering. The first method tested in this work enumerates through a large number of HFUs scenarios using 10 clustering algorithms and different input parameters (number of clusters, minimum number of samples, etc.). The scenario with the largest average correlation coefficient is selected as optimum. The second method uses the DE algorithm to maximize the average correlation coefficient and hence obtain the optimal HFUs. The third method consists of two stages. The first stage uses the OPTICS clustering algorithm to determine the number of HFUs, while the second stage generates the desired clusters using the Gaussian mixture algorithm. Both iterative evaluation and DE optimization methods achieved the same clustering results. However, DE optimization resulted in 85% reduction in runtime due to the robust search capability of the DE algorithm which leads to the solution more efficiently. Furthermore, another significant reduction in runtime was achieved using the two-stage clustering method which yielded very close results. The proposed methods in this work provide unique and potential opportunity to improve the use of FZI data analysis to identify HFUs. This work uses the power of clustering and stochastic algorithms to support a critical process in reservoir characterization.

3 citations

Posted ContentDOI
31 May 2023
TL;DR: In this article , the authors used the fuzzy c-means clustering technique to identify rock types in 280 core samples obtained from one of the wells drilled in the Asmari reservoir located in the Mansouri field.
Abstract: Abstract Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples obtained from one of the wells drilled in the Asmari reservoir located in the Mansouri field. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. Amounts of porosity and permeability of the different reservoir rock samples have high dispersion; the relationship between these two parameters improves by using hydraulic flow unit techniques significantly. In this study, the relationship between porosity and permeability of correlation coefficient improves and increases in each hydraulic flow unit by using the flow zone index method so that in the general case for all samples increased from 0.55 to 0.81 in the first hydraulic flow unit, 0.94 in the second hydraulic flow unit, 0.85 in the third hydraulic flow unit and 0.94 in the fourth hydraulic flow unit that this is because the samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.
Journal ArticleDOI
02 Dec 2022-Energies
TL;DR: In this paper , the results imply that fines migration due to formation erosion is one of the key processes that must be better understood and controlled in order to mitigate injectivity issues at the study area.
Abstract: The Upper Pannonian (UP) sandstone formation has been utilised for thermal water production in Hungary for several decades. Although sustainable utilisation requires the reinjection of cooled geothermal brine into the host rock, only a fraction of the used water is reinjected in the country. UP sandstone formation is reported to exhibit low injectivity, making reinjection challenging, and its petrophysical properties are poorly known, which increases uncertainty in designing operational parameters. The goal of the study is to provide experimental data and to gain a better understanding of formation characteristics that control injectivity and productivity issues in Upper Pannonian sandstone layers. Petrographical characterisation and petrophysical laboratory experiments are conducted on cores retrieved from two wells drilled in the framework of an R&D project at the depth of between 1750 m and 2000 m. The experiments, such as grain density, porosity, permeability, and ultrasonic velocity, as well as thin section, grain size distribution, XRD, and SEM analyses, are used to determine Petrophysical Rock Types (PRT) that share distinct hydraulic (flow zone indicator, FZI) and petrophysical characteristics. These are used to identify well intervals with lower potential for injectivity issues. The results imply that fines migration due to formation erosion is one of the key processes that must be better understood and controlled in order to mitigate injectivity issues at the study area. Future investigation should include numerical and experimental characterisation of formation damage, including water–rock interaction tests, critical flow velocity measurements, and fines migration analysis under reservoir conditions.
Posted ContentDOI
13 Jun 2023
TL;DR: In this paper , the authors used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran.
Abstract: Abstract Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.
Proceedings ArticleDOI
15 Aug 2022
TL;DR: Wenhao Zheng et al. as mentioned in this paper proposed a data-feature-policy solution for multiscale geological-geophysical intelligent reservoir characterization, which has the advantages of data-driven, objectivity, and compatibility.
Abstract: PreviousNext You have accessSecond International Meeting for Applied Geoscience & EnergyA "data-feature-policy" solution for multiscale geological-geophysical intelligent reservoir characterizationAuthors: Wenhao ZhengFei TianQingyun DiJiangyun ZhangHui ZhouWang ZhangZhongxing WangWenhao ZhengChinese Academy of SciencesSearch for more papers by this author, Fei TianChinese Academy of SciencesSearch for more papers by this author, Qingyun DiChinese Academy of SciencesSearch for more papers by this author, Jiangyun ZhangChinese Academy of SciencesSearch for more papers by this author, Hui ZhouPetroChinaSearch for more papers by this author, Wang ZhangChinese Academy of SciencesSearch for more papers by this author, and Zhongxing WangChinese Academy of SciencesSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3748948.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractInnovative interpretation methods based on efficient measurements are the key to establishing accurate “geological-geophysical” mappings. The multi-solvability of petrophysical responses leads to the complex non-linear relationship between multi-scale geophysical data and the classification of strongly heterogeneous geological bodies, which makes it difficult for quantitative reservoir characterization. As multi-source geological-geophysical data have inherent differences in resolution and properties, how to effectively fuse these data becomes a real challenge in the big data era.Based on understanding the intrinsic correlation of different measurements on the given geo-body, a combination of machine learning algorithms was established for multi-scale geological-geophysical intelligent reservoir characterization, which was named “Data-Feature-Policy” solution. (1) Data-level fusion: The dataset was obtained from the conventional logging data of 921m deep carbonate rock in the Tarim Basin. Firstly, the Isolated Forest algorithm was used for quality control to remove outliers, and then, the multi-dimensional parameters were reduced to a 2-dimensional embedding vector by t-SNE algorithm. (2) Feature-level extraction: Based on the Density Peak Clustering algorithm, non-spherical clusters of 2-dimensional embedding vectors were clustered. Then, calibrated by the core-electrical imaging chart, each cluster was labeled electrofacies classification; (3) Policy-level characterization: Based on Deep Belief Network, the geological prediction model was established. It was optimized by a double-loop filtering mechanism that selected the parameter-cluster combination with the highest correct rate, provided that each accuracy is greater than 80%. And the final accuracy rate exceeded 93%. The model was used to quantitatively identify the 3176m carbonate reservoir, automatically count the development thickness and distribution range of reservoir classification, which clarified the reservoir vertical structure and distribution around the wellbore.The “Data-Feature-Policy” solution has the advantages of data-driven, objectivity, and compatibility. It can dig high-dimensional mapping relationships of geological-geophysical data at the feature level, which helps to better understand the multi-variate and multi-attribute data fusion. This effective workflow is suitable to characterize heterogeneous reservoirs intelligently.Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2022 in Houston, Texas.Keywords: data-feature-policy, machine learning, reservoir characterizationPermalink: https://doi.org/10.1190/image2022-3748948.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished: 15 Aug 2022 CITATION INFORMATION Wenhao Zheng, Fei Tian, Qingyun Di, Jiangyun Zhang, Hui Zhou, Wang Zhang, and Zhongxing Wang, (2022), "A "data-feature-policy" solution for multiscale geological-geophysical intelligent reservoir characterization," SEG Technical Program Expanded Abstracts : 3272-3276. https://doi.org/10.1190/image2022-3748948.1 Plain-Language Summary Keywordsdata-feature-policymachine learningreservoir characterizationPDF DownloadLoading ...