3D geological modeling of fractured volcanic reservoir bodies in Block DX18 in Junggar Basin, NW China

Wu, Junjun; Sun, Yuanhui; Wang, Bin; Wang, Yongjun; Xu, Lei; Dai, Chunhui

doi:10.1016/s1876-3804(12)60020-2

Cited by 29 publications

(6 citation statements)

References 3 publications

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“…This is also similar to environments of volcanoes in the Upper Carboniferous in Santanghu, Kelameili, and Wucaiwan (Z. Chen et al, 2016;Luo et al, 2016;J. Wu et al, 2012) which are in the nearby Mulei Depression.…”

Section: Effects and Resultssupporting

confidence: 69%

“…Deep magma in the continent's edge fiercely uplifted, resulting in massive volcanic eruptions, forming volcanoes of large‐scale and multistage composite superimposition (D. Li, He, Tang, Fan, & Kong, ; W. Z. Zhao, Zou, et al, ). This is also similar to environments of volcanoes in the Upper Carboniferous in Santanghu, Kelameili, and Wucaiwan (Z. Chen et al, ; Luo et al, ; J. Wu et al, ) which are in the nearby Mulei Depression.…”

Section: Distribution Of Volcanoessupporting

confidence: 74%

“…In recent years, the exploration of volcanic rocks has received more and more attention (M. Li, Zou, & Liu, ; Pu, Zhong, & Zhang, ; Yang, Zhang, Liu, Wen, & Xu, ; C. Zhang, Shi, & Wu, ; Zhu, ; Zou et al, ). For identification of volcanic cones or volcanic rocks, previous research on this subject (Y. Li, He, & Liu, ) mainly used the seismic facies characteristic (Y. Feng et al, ; Qin, Wei, & Zhang, ; Yu, ), seismic attribute analysis (J. Wu et al, ; Zhu, ), coherent analysis (Guo et al, ), and other methods (Alvarenga, Iacopini, Kuchle, Scherer, & Goldberg, ; Z. Q. Feng, ; Malehmir & Bellefleur, ). However, these studies were subject to the quality of seismic data and often had to constrain a large amount of well data (Shi et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Feng et al, 2016;Qin, Wei, & Zhang, 2016;Yu, 2008), seismic attribute analysis (J. Wu et al, 2012;Zhu, 2013), coherent analysis (Guo et al, 2016), and other methods (Alvarenga, Iacopini, Kuchle, Scherer, & Goldberg, 2016;Z. Q. Feng, 2008;Malehmir & Bellefleur, 2009).…”

mentioning

confidence: 99%

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Identification method and distribution of palaeovolcanoes in Mulei Depression in the Junggar Basin, Western China

2019

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case of minimal well information available, we identify the volcanoes of the Upper Carboniferous Bashan Formation of the Mulei Depression and clarify its distribution characteristics. Through frequency decomposition and a reconstruction of seismic data, the seismic reflection characteristics of the ancient volcanic cone become prominent and easy to identify, and even the period of some stacked volcanic cones can be divided. According to the geological model put forward, the seismic reflection characteristics of the ancient volcanic cone and its surrounding volcanic rocks are clarified. The volcanic cones are characterized by mound-clutter and weak-blank amplitude reflection. The internal clutter mainly have resulted from rapid changes in lithology and complex secondary faults. Away from the crater direction, the energy of a seismic event is gradually enhanced, and the volcanic sediments from other sources will be superimposed above the previously formed volcanoes. It is found that the volcanic channel with a short-axis continuous illusion is often developed at the bottom. The volcanic channel which can be identified in seismic mainly affects the development of large volcanoes. Finally, 27 volcanic cones and seven volcanic channels were identified via establishing the threedimensional (3D) model of the volcano and the amplitude of the root mean square of seismic data after reconstruction, as well as interpreting deep faults. The volcanic development in the Mulei Depression has the characteristics of an NE spread along deep faults and northern more than southern.

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Section: Effects and Resultssupporting

confidence: 69%

Section: Distribution Of Volcanoessupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Identification method and distribution of palaeovolcanoes in Mulei Depression in the Junggar Basin, Western China

2019

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“…One of the applications of modelling software in geology is building a 3D model of geological structure for an investigated region [1][2][3][4][5][6][7][8][9][10][11][12][13]. Using the 3D models the potential mineral target can be predicted and evaluating mineral resources can be performed [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

3D model of Sin Quyen iron oxide copper deposit in Lao Cai, North Vietnam

2018

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The paper presents a 3D model of ore bodies of the Sin Quyen iron oxide copper-gold deposit in Lao Cai province, North Vietnam. To build 3D model we used the computer software MineScape and archival geological, geophysical data recorded from 146 boreholes, 21 tunnels and 216 trenches, including the data obtained from chemical and neutron instrumental activation analysis of 50 ore samples collected in 2015 year. The modelling process was composed of three stages: first was checking and validating of the input data, second was data processing and the last building 3D model. The model shows that the ore bodies extend below level of -300 m, such information will be used for future exploration. Based on the obtained model, the reserves of selected metals were calculated and equal to 570 000 tonnes for copper and 190 tonnes for uranium. The obtained Cu reserve is comparable with that estimated and published in the archival report of the Vietnam Geological Department.

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Volcanic rocks distribution and basement structure in western–central Junggar Basin revealed by gravitational and magnetic data

et al. 2017

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The Junggar Basin is a large superimposed basin with multistage evolution. The Carboniferous volcanic rock in the middle‐lower part of the basin has been an important target of oil and gas exploration. Therefore, it is crucial to establish a set of efficient methods and techniques to find out the distribution of the Carboniferous volcanic rock and the tectonic factors related to the reservoir forming of the volcanic rock. Our research reveals that the density and magnetism of the Carboniferous volcanic rock are obviously higher than those of the Mesozoic and Cenozoic sedimentary rocks. With a series of frequency domain filters and boundary enhancement techniques, we determine the residual gravitational and magnetic anomalies caused by the Carboniferous volcanic rock. Combined with borehole and seismic data from the studied areas, the horizontal and vertical distributions of the Carboniferous volcanic rock are defined, and the lithologies of different types of volcanic rocks are predicted. Furthermore, the gravitational and magnetic anomalies are used to estimate the basement faults and topography. The regional deep faults and their secondary faults of the basement are outlined, and the model of basement relief is constructed. Finally, the effects of the fracture structure and the basement topography in the process of volcanic activity and hydrocarbon accumulation are fully discussed. These results provide fundamental information for optimal selection of the favorable area of volcanic reservoir.

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3D geological modeling of fractured volcanic reservoir bodies in Block DX18 in Junggar Basin, NW China

Cited by 29 publications

References 3 publications

Identification method and distribution of palaeovolcanoes in Mulei Depression in the Junggar Basin, Western China

Identification method and distribution of palaeovolcanoes in Mulei Depression in the Junggar Basin, Western China

3D model of Sin Quyen iron oxide copper deposit in Lao Cai, North Vietnam

Volcanic rocks distribution and basement structure in western–central Junggar Basin revealed by gravitational and magnetic data

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