Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801325
Z. Koren, I. Ravve
{"title":"Eigenray Tracing in 3D Heterogeneous Media","authors":"Z. Koren, I. Ravve","doi":"10.3997/2214-4609.201801325","DOIUrl":"https://doi.org/10.3997/2214-4609.201801325","url":null,"abstract":"","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116965871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801186
M. Endo, M. Zhdanov, A. Gribenko, D. Sunwall, Takuya Miura, Hisako Mochinaga, N. Aoki, T. Mouri
Summary Exploration and development of geothermal fields requires quantitative interpretation of different geophysical data. In this paper, we introduce the novel approach to three-dimensional inversion and interpretation of multimodal geophysical data, which incorporates the known geological/geophysical constraints. In a general case, geophysical inverse problem is ill-posed, i.e., it is non-unique and unstable; however, appropriate a priori information can help reducing the nonuniqueness and increasing the stability of this ill-posed problem. The developed approach uses the principles of inversion “guided” by known information. In the framework of this approach, the 3D inversion itself is data driven, but a priori geological/geophysical model is used as the initial and reference model during the iterative inversion process. We have applied the developed method to the integrated interpretation of magnetotelluric, gravity, and magnetic data in Yamagawa geothermal field of Japan, using the constraints based on seismic and well-logging data acquired in the same area. The results have demonstrated that the developed method produces reliable 3D models of different physical properties, which provide a solid basis for geological interpretation of the complex subsurface structures in the survey area, critical for geothermal exploration.
{"title":"Three-Dimensional Inversion and Interpretation of Multimodal Geophysical Data in Yamagawa Geothermal Field, Japan","authors":"M. Endo, M. Zhdanov, A. Gribenko, D. Sunwall, Takuya Miura, Hisako Mochinaga, N. Aoki, T. Mouri","doi":"10.3997/2214-4609.201801186","DOIUrl":"https://doi.org/10.3997/2214-4609.201801186","url":null,"abstract":"Summary Exploration and development of geothermal fields requires quantitative interpretation of different geophysical data. In this paper, we introduce the novel approach to three-dimensional inversion and interpretation of multimodal geophysical data, which incorporates the known geological/geophysical constraints. In a general case, geophysical inverse problem is ill-posed, i.e., it is non-unique and unstable; however, appropriate a priori information can help reducing the nonuniqueness and increasing the stability of this ill-posed problem. The developed approach uses the principles of inversion “guided” by known information. In the framework of this approach, the 3D inversion itself is data driven, but a priori geological/geophysical model is used as the initial and reference model during the iterative inversion process. We have applied the developed method to the integrated interpretation of magnetotelluric, gravity, and magnetic data in Yamagawa geothermal field of Japan, using the constraints based on seismic and well-logging data acquired in the same area. The results have demonstrated that the developed method produces reliable 3D models of different physical properties, which provide a solid basis for geological interpretation of the complex subsurface structures in the survey area, critical for geothermal exploration.","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117134461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801577
C. Song, T. Alkhalifah, Z. Wu
Summary Attaining information of the source mechanism involved in micro-seismic events will greatly help us understand the reservoir fracturing and the stress evolved. The components of moment tensor can tell us the information involving magnitudes, modes, and orientations of fractures. Meanwhile, its singular value decomposition (SVD) exposes the difference between three main kinds of source types that may present itself in a moment tensor solution. We propose to use support vector machine (SVM), which is a type of machine learning approach, to classify the source type of a micro-seismic event by using the normalized eigenvalues of moment tensor matrix as classification principal components. The tests on moment tensor matrices based on typical source type and real cases yield reliable classification results.
{"title":"Source Type Classification Based On the Support Vector Machine Method","authors":"C. Song, T. Alkhalifah, Z. Wu","doi":"10.3997/2214-4609.201801577","DOIUrl":"https://doi.org/10.3997/2214-4609.201801577","url":null,"abstract":"Summary Attaining information of the source mechanism involved in micro-seismic events will greatly help us understand the reservoir fracturing and the stress evolved. The components of moment tensor can tell us the information involving magnitudes, modes, and orientations of fractures. Meanwhile, its singular value decomposition (SVD) exposes the difference between three main kinds of source types that may present itself in a moment tensor solution. We propose to use support vector machine (SVM), which is a type of machine learning approach, to classify the source type of a micro-seismic event by using the normalized eigenvalues of moment tensor matrix as classification principal components. The tests on moment tensor matrices based on typical source type and real cases yield reliable classification results.","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117204631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201800979
Y. Qu, J. Li, Z. Li, J. Huang
{"title":"P-And S-Wave Separated Elastic Reverse Time Migration for OBS Data from Fluid-Solid Media with Irregular Seabed Interfac","authors":"Y. Qu, J. Li, Z. Li, J. Huang","doi":"10.3997/2214-4609.201800979","DOIUrl":"https://doi.org/10.3997/2214-4609.201800979","url":null,"abstract":"","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801424
O. Petrovskyy, T. O. Fedchenko, N. S. Gangenko, V. Benko
{"title":"Subsalt 3-D Modelling and HC Reservoir Prediction with Scarce 2-D Seismic Datasets: Can We Obtain Reliable Results?","authors":"O. Petrovskyy, T. O. Fedchenko, N. S. Gangenko, V. Benko","doi":"10.3997/2214-4609.201801424","DOIUrl":"https://doi.org/10.3997/2214-4609.201801424","url":null,"abstract":"","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"17 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120859688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801256
B. Sorani
{"title":"Geological Approach to the Optimization of Production Rates by Identifying Depositional Systems","authors":"B. Sorani","doi":"10.3997/2214-4609.201801256","DOIUrl":"https://doi.org/10.3997/2214-4609.201801256","url":null,"abstract":"","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201800634
B. Lowney, I. Lokmer, C. Bean, G. O'Brien, M. Igoe
A seismic image is formed by interactions of the seismic wavefield with geological interfaces, in the form of reflections, diffractions, and other coherent noise. While in conventional processing workflows reflections are favoured over diffractions, this is only beneficial in areas with uniform stratigraphy. Diffractions form as interactions of the wavefield with discontinuities and therefore can be used to image them. However, to image diffractions, they must first be separated from the seismic wavefield. Here we propose a pattern recognition approach for separation, employing image segmentation. We then compare this to two existing diffraction imaging methods, plane-wave destruction and f-k filtering. Image segmentation can be used to divide the image into pixels which share certain criteria. Here, we have separated the image first by amplitude using a histogram-based segmentation method, followed by edge detection with a Sobel operator to locate the hyperbola. The image segmentation method successfully locates diffraction hyperbola which can then be separated and migrated for diffraction imaging. When compared with plane-wave destruction and f-k filtering, the image segmentation method proves beneficial as it allows for identification of the hyperbolae without noise. However, the method can fail to identify hyperbolae in noisier environments and when hyperbolae overlap.
{"title":"An Outlook on Seismic Diffraction Imaging Using Pattern Recognition","authors":"B. Lowney, I. Lokmer, C. Bean, G. O'Brien, M. Igoe","doi":"10.3997/2214-4609.201800634","DOIUrl":"https://doi.org/10.3997/2214-4609.201800634","url":null,"abstract":"A seismic image is formed by interactions of the seismic wavefield with geological interfaces, in the form of reflections, diffractions, and other coherent noise. While in conventional processing workflows reflections are favoured over diffractions, this is only beneficial in areas with uniform stratigraphy. Diffractions form as interactions of the wavefield with discontinuities and therefore can be used to image them. However, to image diffractions, they must first be separated from the seismic wavefield. Here we propose a pattern recognition approach for separation, employing image segmentation. We then compare this to two existing diffraction imaging methods, plane-wave destruction and f-k filtering. \u0000\u0000Image segmentation can be used to divide the image into pixels which share certain criteria. Here, we have separated the image first by amplitude using a histogram-based segmentation method, followed by edge detection with a Sobel operator to locate the hyperbola. \u0000\u0000The image segmentation method successfully locates diffraction hyperbola which can then be separated and migrated for diffraction imaging. When compared with plane-wave destruction and f-k filtering, the image segmentation method proves beneficial as it allows for identification of the hyperbolae without noise. However, the method can fail to identify hyperbolae in noisier environments and when hyperbolae overlap.","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801453
E. Chabyshova, G. Goloshubin, S. Ahmad
{"title":"The Impact of Reservoir Heterogeneities on P-wave Propagation at Low Frequencies – Examples from the North Sea","authors":"E. Chabyshova, G. Goloshubin, S. Ahmad","doi":"10.3997/2214-4609.201801453","DOIUrl":"https://doi.org/10.3997/2214-4609.201801453","url":null,"abstract":"","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125801432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-11DOI: 10.3997/2214-4609.201801559
Edo Pratama, M. Ismail, S. Ridha
Injecting CO2 with huff-n-puff process is becoming an efficient technique to overcome some productivity damage and enhanced gas recovery in depleted gas reservoirs. Several numerical simulations are required to estimate the gas recovery factor obtained from CO2 huff-n-puff injection. However, numerical simulations are very expensive and time-consuming processes.
{"title":"Development of a Predictive Model for Enhanced Gas Recovery by CO2 Huff-n-Puff in Depleted Carbonate Gas Reservoirs","authors":"Edo Pratama, M. Ismail, S. Ridha","doi":"10.3997/2214-4609.201801559","DOIUrl":"https://doi.org/10.3997/2214-4609.201801559","url":null,"abstract":"Injecting CO2 with huff-n-puff process is becoming an efficient technique to overcome some productivity damage and enhanced gas recovery in depleted gas reservoirs. Several numerical simulations are required to estimate the gas recovery factor obtained from CO2 huff-n-puff injection. However, numerical simulations are very expensive and time-consuming processes.","PeriodicalId":325587,"journal":{"name":"80th EAGE Conference and Exhibition 2018","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126195409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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