Chemometric Classification of Crude Oils in Complex Petroleum Systems Using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm

Tao, Keyu; Cao, Jian; Wang, Yuce; Mi, Julei; Ma, Wanyun; Shi, Chunhua

doi:10.1021/acs.energyfuels.0c01333

Cited by 6 publications

(3 citation statements)

References 61 publications

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“…Conventional methods are difficult to solve, and artificial intelligence technology still faces both opportunities and challenges in the future. In the future, artificial intelligence technology can be effectively combined with oil and gas field development to achieve the optimal solution of complex problems in intelligent oil and gas reservoir development using machine learning and deep learning methods. , In the future, attention should be paid to the “three modernizations” innovation of standardization of oil and gas development data, intelligence of oil and gas fields, and platform collaboration, in order to achieve leapfrog development and rapid upgrading of artificial intelligence in the oil and gas industry. , …”

Section: Prospects For the Future Development Of Artificial Intellige...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

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Artificial intelligence technology will be increasingly applied in the oil and gas industry. The rapid development of artificial intelligence technology can solve problems such as high environmental sensitivity and complex production processes in the oil and gas industry. In recent years, emerging technologies represented by artificial intelligence have developed rapidly, assisting petroleum enterprises in digital transformation and intelligent upgrading. This article elaborates on the development trend of artificial intelligence technology. Based on the business scenarios and characteristics of the oil and gas industry, the application status of artificial intelligence technology in domestic and foreign petroleum technology service enterprises was summarized and analyzed. The application scenarios of artificial intelligence technology in the fields of dynamic analysis of oil and gas reservoirs, intelligent historical fitting, numerical simulation proxy models, and production plan optimization were analyzed with emphasis. Based on the problems and challenges faced in the development process of oil and gas reservoirs, it is proposed that petroleum enterprises should attach importance to the "three modernizations" innovation of data standardization, oil and gas field intelligence, and platform collaboration, in order to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development. On this basis, prospects for future artificial intelligence technology are proposed, pointing out that the development of artificial intelligence technology will be faster and faster, and there will be higher demand for artificial intelligence technology in the construction of digital oil and gas fields in China in the future. The research results have important reference value for the development of the oil and gas industry.

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Section: Prospects For the Future Development Of Artificial Intellige...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

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“…Among them, hierarchical clustering analysis (HCA) and principal component analysis (PCA) are the two commonly used methods (Peters et al, 2005;Asemani and Rabbani, 2021). Additionally, the method for oil-oil and oil-source rock correlations also includes factor analysis (Zumberge, 1987;Chakhmakhchev et al, 1996), star diagram (Justwan et al, 2006;Mashhadi and Rabbani, 2015), K-nearest neighbor (Peters et al, 2007;Peters et al, 2008), multidimensional scaling (Wang et al, 2016;Wang et al, 2020a), discriminant analysis (Zhang et al, 2019;Shi et al, 2020), t-distributed stochastic neighbor embedding (Tao et al, 2020). Meanwhile, geochemists try to introduce new methods (Asemani and Rabbani, 2021), indicating that chemometrics has great potential in coping with the problem of geochemical correlation.…”

Section: Introductionmentioning

confidence: 99%

The Geochemical Study of Oil-Oil and Oil-Source Rock Correlations in the Wushi Sag of the Beibu Gulf Basin, South China Sea

Wang

Xue²,

Zhou

et al. 2022

Front. Earth Sci.

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Chemometrics has been widely used to cope with the problems of oil-oil and oil-source correlations because of its unique advantages in the comprehensive consideration of multiple parameters and the classification of samples or variables. In this paper, three chemometric methods, especially multidimensional scaling, were used to revisit the genetic oil family and the relationship between the crude oil and the source rock, because the oil source in the Wushi Sag, a significant petroliferous sag in the Beibu Gulf Basin of South China Sea, is still controversial. Two genetic families of crude oils, namely group A and group B, have been identified based on chemometric results. Group A oils are characterized by relatively higher Pr/Ph ratios and a high abundance of C27 ααα 20R steranes and C30-methylsteranes than those of group B oils, suggesting that this group of oils was deposited under a more oxic condition with more contribution of algae organic matter. Group A oils have been interpreted to be a mixture derived from the member 2 and member 3 of the Liushagang Formation (LS-2 and LS-3), whereas group B oils can be ascribed to the LS-2 member. The contribution of LS-3 mudstone member to the Wushi oils in previous studies may have been underestimated to some extent, which was inferred from the chemometric oil-source correlation results. The results of oil-source rock correlation may be used to guide future petroleum exploration activities with the incorporation of geological evidence. The spatial distribution of oil and gas reservoirs varies with burial depth. Taking into consideration other geological evidence, we may infer that the reservoir in eastern Wushi Sag was mainly distributed in the deep of Liushagang Formation, whereas the reservoir in southeast Wushi Sag was likely located in the shallow Liushagang Formation.

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“…Two chemometric methods, hierarchical clustering analysis and principal component analysis (PCA), have been most widely used in petroleum geochemistry (Eneogwe and Ekundayo, 2003;Farrimond et al, 2015;Hao et al, 2010;Peters et al, 2016) to investigate the problems in oil-oil and oil-source rock correlations (Peters et al, 2005). Chemometric methods used in the study of oil -oil and oil -source rock correlations also include R-and Qmodel factor analysis (Sofer, 1984;Zumberge, 1987;Engel et al, 1988;Chakhmakhchev et al, 1996;Scotchman et al, 1998), star diagram (Justwan et al, 2006;Mashhadi and Rabbani, 2015), K-nearest neighbour (Peters et al, 2000(Peters et al, , 2007(Peters et al, , 2008, multidimensional scaling (MDS) (Wang et al, 2016;Wang et al, 2018a), discriminant analysis (Zhang et al, 2019), and t-distributed stochastic neighbor embedding (Tao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Oil chemometrics and geochemical correlation in the Weixinan Sag, Beibuwan Basin, South China Sea

Wang

Xue

Luo

et al. 2020

Energy Exploration & Exploitation

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The oil–oil and oil–source rock correlations, also termed as geochemical correlations, play an essential role in the construction of petroleum systems, guidance of petroleum exploration, and definition of reservoir compartments. In this study, the problems arising from oil–oil and oil–source rock correlations were investigated using chemometric methods on oil and source rock samples from the WZ12 oil field in the Weixinan sag in the Beibuwan Basin. Crude oil from the WZ12 oil field can be classified into two genetic families: group A and B, using multidimensional scaling and principal component analysis. Similarly, source rocks of the Liushagang Formation, including its first, second, and third members, can be classified into group I and II, corresponding to group B and A crude oils, respectively. The principle geochemical parameters in the geochemical correlation for the characterisation and classification of crude oils and source rocks were 4MSI, C27Dia/C27S, and C24 Tet/C26 TT. This study provides insights into the selection of appropriate geochemical parameters for oil–oil and oil–source rock correlations, which can also be applied to other sedimentary basins.

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Chemometric Classification of Crude Oils in Complex Petroleum Systems Using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm

Cited by 6 publications

References 61 publications

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

The Geochemical Study of Oil-Oil and Oil-Source Rock Correlations in the Wushi Sag of the Beibu Gulf Basin, South China Sea

Oil chemometrics and geochemical correlation in the Weixinan Sag, Beibuwan Basin, South China Sea

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