شناسایی و مدل‌سازی گنبد نمکی در داده‌های لرزه‌ای با استفاده از گرادیان بافت سه‌بعدی

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 دانشیار، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران

2 دانشیار، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران موسسه ژئوفیزیک، انستیتوی صنعتی کارلسروهه، کالسروهه، آلمان

3 استادیار، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران

چکیده

به دلیل ساختار پیچیده گنبدهای نمکی که با تغییرات شدید سرعتی همراه است، تعیین محدوده گنبدهای نمکی برای افزایش دقت تصویر‌سازی زیرسطحی لرزه‌ای در مناطق حاوی آنها از اهمیت بسزایی برخوردار است. همچنین با توجه به کاربردهای متعددی که این ساختارهای دیاپیر شکل در صنایع مختلف می­توانند داشته باشند، تعیین محدوده این ساختارهای نمکی، یکی از چالش­های پیش روی پردازشگران و مفسران داده­های لرزه‌ای است. نشانگرهای بافتی لرزه‌ای، یکی از ابزارهای لرزه‌ای متداول برای این منظور هستند. انواع مختلفی از نشانگر­های بافتی معرفی شده‌اند که هرکدام مزایا و معایب خاص خود را دارند و نتایج به‌کارگیری آنها با عدم قطعیت (معمولاً زیاد) همراه است. نشانگر گرادیان بافت یکی از نشانگر­های بافتی است که بر مبنای محاسبه کمّی تغییرات بافت استوار است و به‌راحتی مرز تغییرات بافتی را مشخص می­کند. پس از تعیین مرز این تغییرات، با اِعمال آستانه­گذاری و دوتایی کردن نتیجه گرادیان بافت، می­توان با ابزارهای ریخت­شناسی و توسعه ناحیه‌ای و انتخاب یک نقطه مبنا به­عنوان گنبد نمکی، محدوده گنبد نمکی را شناسایی کرد و مدل ساختاری آن را به‌دست­آورد. نتایج اعمال روش روی مدل مصنوعی و یک داده لرزه‌ای نشان داد این نشانگر می­تواند جایگزینی برای نشانگر­های متداول جهت تفکیک بافت­های مختلف و تعیین محدوده گنبد نمکی باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identification and modeling of salt dome in seismic data using three-dimensional texture gradient

نویسندگان [English]

  • Amin Roshandel Kahoo 1
  • Mehrdad Soleimani Monfared 2
  • Mohammad Radad 3
1 Associate professor, Faculty of Mining, petroleum and Geophysics, Shahrood University of technology, Shahrood, Iran
2 Associate professor, Faculty of Mining, petroleum and Geophysics, Shahrood University of technology, Shahrood, Iran Geophysical Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany
3 Assistant Professor, Faculty of Mining, petroleum and Geophysics, Shahrood University of technology, Shahrood, Iran
چکیده [English]

Salt dome is a diapir shaped structure of salt that intrudes vertically through sediment layers and surrounding strata due to its low density. Salt area identification, determining its boundaries and its 3D modeling in seismic data is a crucial issue in the literature of the seismic data interpretation. Due to its high impermeability characteristic, it can form stratigraphic oil traps by sealing the hydrocarbon reservoirs and also could be used as underground storage for natural gas and disposal sites for hazardous waste such as isolation nuclear waste and creation of the compressed air reservoir. The steeply dipping complex-shaped structures related to the salt movement and significant difference in seismic wave propagation velocity inside the salt dome with the enclosed media, imposes significant challenges for seismic data processing and interpretation. Identification and delineation of salt body is a key step in seismic data processing and interpretation, which can help geophysicist to overcome aforementioned problems. In reflection seismic methods, salt boundaries are more often characterized by change of seismic character of the signal also called texture. There are several methods available for texture analysis in image processing that can be divided into seven classes which are statistical analysis, structural methods, transform based approaches, model-based methods, graph-based techniques, learning based strategies and entropy-based methods. Textural attributes characterize the spatial arrangement of neighboring amplitudes. Extraction of seismic texture attributes can be performed using spectral information of image such as Gabor filters and local 2D Fourier spectra. Dip, similarity and coherence are the common structural attributes which are used generally for textural analysis in seismic data. The most common rational approach to describe the texture in seismic image is to measure the statistical properties of the image. Gray Level Co-occurrence Matrix, chaos and variance are three conventional statistical seismic texture attributes used for this purpose. Due to the textural contrast of the salt dome with the surrounding layers and sediments, edge detection tools can also be used to determine the boundaries of textural changes and delineate the salt area. In this study, we used a new textural seismic attribute known as the gradient of texture to characterize the change of seismic character between the salt body and its surrounding geology. It calculates the texture gradient in two adjacent windows around a sample in different directions. It is supposed that different area in seismic image with different textural pattern will exhibit diverse gradient of texture. Thus, it will be appropriate for image segmentation for specific interpretation investigation. The gradient of texture attribute will differentiate desired area from the rest of the image through supervised classification and growth strategy in extending the selected classes. Efficiency of the introduced method for salt dome delineation and modeling in seismic data was investigated here by applying on a synthetic model and 3D seismic data from the Persian Gulf. Comparison between obtained results of the proposed method and conventional attributes revealed superiority of the 3D texture gradient in textural segmentation and salt dome modeling from seismic data.
 

کلیدواژه‌ها [English]

  • Salt dome
  • seismic attribute
  • gradient of texture
  • morphology
باقری، ح.، 1395، گنبدهای نمکی: انتشارات نظری، 120 صفحه، تهران، ایران.
پاژنگ، س.، کدخدایی، ع.، زمانی، ب.، برگریزان، م.، یوسف‌پور، م.، 1394، معرفی ۱۷ گنبد نمکی مدفون و غیرمدفون بر اساس داده­های لرزه‌ای در تنگه هرمز (بلوک E): پژوهش نفت،84، 150-160.
Adams, R., and Bischof, L., 1994, Seeded region growing: IEEE Transactions on pattern analysis and machine intelligence, 16(6), 641-647, http://dx.doi.org/10.1109/34.295913.
Amin, A., and Deriche, M., 2015a, A hybrid approach for salt dome detection in 2D and 3D seismic data: Paper read at IEEE International Conference on Image Processing (ICIP).
Amin, A., and Deriche, M., 2015b, A new approach for salt dome detection using a 3D multidirectional edge detector: Applied Geophysics, 12(3), 334-342, http://dx.doi.org/10.1007/s11770-015-0512-2.
Amin, A., Deriche, M., Hegazy, T., Wang, Z., and AlRegib, G., 2015, A novel approach for salt dome detection using a dictionary-based classifier: SEG Technical Program Expanded Abstracts 2015, Society of Exploration Geophysicists, 1816-1820.
Aqrawi, A. A., Boe, T. H., and Barros, S., 2011, Detecting salt domes using a dip guided 3D Sobel seismic attribute: SEG Technical Program Expanded Abstracts 2011, Society of Exploration Geophysicists, 1014-1018.
Asgharzadeh, M., Hashemi, H., and von Frese, R. R., 2018, Comprehensive gravitational modeling of the vertical cylindrical prism by Gauss–Legendre quadrature integration: Geophysical Journal International, 212(1), 591-611, http://dx.doi.org/10.1093/gji/ggx413.
Barnes, A. E., 2016, Handbook of poststack seismic attributes: Society of Exploration Geophysicists.
Bergqvist, G., and Larsson, E. G., 2010, The higher-order singular value decomposition: Theory and an application [lecture notes]: IEEE Signal Processing Magazine, 27(3), 151-154, http://dx.doi.org/10.1109/MSP.2010.936030.
Berthelot, A., Solberg, A. H., and Gelius, L. J., 2013, Texture attributes for detection of salt: Journal of Applied Geophysics, 88, 52-69, http://dx.doi.org/10.1016/j.jappgeo.2012.09.006.
Berthelot, A., Solberg, A. H., Morisbak, E., and Gelius, L. J., 2011, Salt diapirs without well defined boundaries–a feasibility study of semi‐automatic detection: Geophysical Prospecting, 59(4), 682-696, http://dx.doi.org/10.1111/j.1365-2478.2011.00950.x.
Di, H., Gao, D., and AlRegib, G., 2019, Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification: Geophysical Journal International, 218(2), 1262-1275, http://dx.doi.org/10.1093/gji/ggz226.
Eichkitz, C. G., Amtmann, J., and Schreilechner, M. G., 2013, Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions: Computers & Geosciences, 60, 176-183, http://dx.doi.org/10.1016/j.cageo.2013.07.006.
Eichkitz, C. G., Schreilechner, M. G., de Groot, P., and Amtmann, J., 2014, Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes: Interpretation, 3(1), T13-T23, http://dx.doi.org/10.1190/INT-2014-0099.1.
Farrokhnia, F., Roshandel Kahoo, A., and Soleimani, M., 2018, Automatic salt dome detection in seismic data by combination of attribute analysis on CRS images and IGU map delineation: Journal of Applied Geophysics, 159, 395-407, http://dx.doi.org/10.1016/j.jappgeo.2018.09.018.
Gao, D., 2011, Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: a review: Geophysics, 76(2), W1-W13, http://dx.doi.org/10.1190/1.3553479.
Haralick, R. M., and Shanmugam, K., 1973, Textural features for image classification: IEEE Transactions on Systems, Man, and Cybernetics, 6, 610-621, http://dx.doi.org/10.1109/TSMC.1973.4309314.
Hegazy, T., and AlRegib, G., 2014, Coherensi: A new full-reference iqa index using error spectrum chaos: paper read at 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
Humeau-Heurtier, A., 2019, Texture feature extraction methods: A survey: IEEE Access, 7, 8975-9000, http://dx.doi.org/10.1109/ACCESS.2018.2890743.
Ikelle, L. T., and Amundsen, L., 2005, Introduction To Petroleum Seismology, Investigations in Geophysics: Society of Exploration Geophysicists.
Jing, Z., Yanqing, Z., Zhigang, C., and Jianhua, L., 2007, Detecting boundary of salt dome in seismic data with edge-detection technique: SEG Technical Program Expanded Abstracts 2007, Society of Exploration Geophysicists, 1392-1396.
Jones, I. F., and Davison, I., 2014, Seismic imaging in and around salt bodies: Interpretation, 2(4), SL1-SL20, http://dx.doi.org/10.1190/INT-2014-0033.1.
Katz, D. L., and Tek, M. R., 1981, Overview on underground storage of natural gas: Journal of Petroleum Technology, 33(06), 943-951, http://dx.doi.org/10.2118/9390-PA.
Oldenburg, C. M., and Pan, L., 2013, Porous media compressed-air energy storage (PM-CAES): Theory and simulation of the coupled wellbore–reservoir system: Transport in porous media, 97(2), 201-221, http://dx.doi.org/10.1007/s11242-012-0118-6.
Osgood, B., 2009, The Fourier transform and its applications, in Lecture notes for Electrical Engineering: Electrical Engineering Department, Stanford University.
Otsu, N., 1979, A threshold selection method from gray-level histograms: IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
Serra, J., and Soille, P., 2012, Mathematical morphology and its applications to image processing, 2: Springer Science & Business Media.
Shafiq, M. A., Wang, Z., AlRegib, G., Amin, A., and Deriche, M., 2017, A texture-based interpretation workflow with application to delineating salt domes: Interpretation, 5(3), SJ1-SJ19, http://dx.doi.org/10.1190/INT-2016-0043.1.
Shafiq, M. A., Wang, Z., Amin, A., Hegazy, T., Deriche, M., and AlRegib, G., 2015, Detection of salt-dome boundary surfaces in migrated seismic volumes using gradient of textures: SEG Technical Program Expanded Abstracts 2015, Society of Exploration Geophysicists, 1811-1815.
Solberg, A., Berthelot, A., and Gelius, L., 2011, Gabor filters for segmentation of salt structures: 73rd EAGE Conference and Exhibition, B041.
Solberg, A. H., and Gelius, L. J., 2011, New texture attributes from local 2D Fourier spectra: Paper read at 81th SEG Annual Meeting.
Visvanathan, C., 1996, Hazardous waste disposal: Resources, conservation and recycling, 16(1), 201-212, http://dx.doi.org/10.1016/0921-3449(95)00057-7.