پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,226 |
| تعداد مقالات | 67,708 |
| تعداد مشاهده مقاله | 115,011,709 |
| تعداد دریافت فایل اصل مقاله | 89,695,015 |
ANFIS Rules Driven Integrated Seismic and Petrophysical Facies Analysis | ||
| فیزیک زمین و فضا | ||
| مقاله 9، دوره 47، شماره 4، بهمن 1400، صفحه 133-141 اصل مقاله (2.34 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2022.331894.1007370 | ||
| نویسندگان | ||
| Marzieh Mirzakhanian1؛ Hosein Hashemi* 2 | ||
| 1Ph.D. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| 2Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Different learning methods have been used to recognize seismic facies and reservoir characterization using seismic attributes. One of the significant issues in automatic facies analysis is to relate the seismic data to facies properties using the well data. According to previous studies, the role of attributes is more significant than the learning method for automatic classification. The proposed method uses supervised selection of seismic attributes for automatic facies analysis. Extended Elastic Impedances (EEI) at different angles as seismic attributes are being increasingly utilized in both seismic facies analysis and reservoir characterization. They are representative of elastic parameters of rocks appropriately. In the presented method, proper EEI seismic attributes are selected after a feasibility study using petro-physical logs, and EEI template analysis of the well data. Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to the fuzzy coded data of the well facies to train an automatic model to predict facies from the seismic data. Subsequently, the same particular EEI attributes are prepared. The EEI attributes from the seismic data are inputs for the trained ANIFIS model to perform seismic facies analysis. In this method, the seismic facies and the well facies are compatible. Only one well data can be sufficient for the well analysis stage and well facies clustering. The proposed method is applied on 3D prestack seismic data located in Abadan plain to discriminate hydrocarbon interval of Sarvak Formation. The results reveal that the supervised selection of attributes and fuzzy concepts present remarkable ability in dealing with imprecise seismic facies analysis and reservoir characterization. | ||
| کلیدواژه‌ها | ||
| Seismic attributes؛ Extended elastic impedance؛ Facies analysis؛ Adaptive neuro- fuzzy inference system | ||
| عنوان مقاله [English] | ||
| ANFIS Rules Driven Integrated Seismic and Petrophysical Facies Analysis | ||
| نویسندگان [English] | ||
| Marzieh Mirzakhanian1؛ Hosein Hashemi2 | ||
| 1Ph.D. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| 2Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Different learning methods have been used to recognize seismic facies and reservoir characterization using seismic attributes. One of the significant issues in automatic facies analysis is to relate the seismic data to facies properties using the well data. According to previous studies, the role of attributes is more significant than the learning method for automatic classification. The proposed method uses supervised selection of seismic attributes for automatic facies analysis. Extended Elastic Impedances (EEI) at different angles as seismic attributes are being increasingly utilized in both seismic facies analysis and reservoir characterization. They are representative of elastic parameters of rocks appropriately. In the presented method, proper EEI seismic attributes are selected after a feasibility study using petro-physical logs, and EEI template analysis of the well data. Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to the fuzzy coded data of the well facies to train an automatic model to predict facies from the seismic data. Subsequently, the same particular EEI attributes are prepared. The EEI attributes from the seismic data are inputs for the trained ANIFIS model to perform seismic facies analysis. In this method, the seismic facies and the well facies are compatible. Only one well data can be sufficient for the well analysis stage and well facies clustering. The proposed method is applied on 3D prestack seismic data located in Abadan plain to discriminate hydrocarbon interval of Sarvak Formation. The results reveal that the supervised selection of attributes and fuzzy concepts present remarkable ability in dealing with imprecise seismic facies analysis and reservoir characterization. | ||
| کلیدواژه‌ها [English] | ||
| Seismic attributes, Extended elastic impedance, Facies analysis, Adaptive neuro- fuzzy inference system | ||
| مراجع | ||
|
Anand, S., Shashi Prakash, Sh., Irfan, A. and Vikas Chand, B., 2018, Fuzzy constrained Lp-norm inversion of direct current resistivity data, Geophysics, 83(1), E11-E24, https://doi.org/10.1190/geo2017-0040.1. Aminzadeh, F. and de Groot, P., 2004, Soft Computing for qualitative and quantitative seismic object and reservoir property prediction, Part 1: Neural Network Applications: First Break, 22 (April), 49–54. Aminzadeh, F. and de Groot, P., 2006, Neural networks and other soft computing techniques with applications in the oil industry: EAGE Publication BV, Houten, 161. Barnes, A. E. and Laughlin, K. J., 2002, Investigation of methods for unsupervised classification of seismic data, 72nd Annual International Meeting, SEG, Expanded Abstracts, 2221- 2224. Brown A. R., 2011, Interpretation of three-dimensional seismic data: Society of Exploration Geophysicists and American Association of Petroleum Geologists, 103–156. Hadiloo, S., Hashemi, H., Mirzaei, S. and Beiranvand, B., 2017, SeisART software: seismic facies analysis by contributing interpreter and computer, Arabian Journal of Geosciences, 10(23), 519. Connolly, P., 1999, Elastic impedance: The Leading Edge, 18, 438-452. Hadiloo, S., Mirzaei, S., Hashemi, H. and Beiranvand, B., 2018, Comparison between unsupervised and supervised fuzzy clustering method in interactive mode to obtain the best result for extract subtle patterns from seismic facies maps, Geopersia, 8(1), 27-34. Hashemi, H., Javaherian, A. and Babuska, R., 2008, A semi-supervised method to detect seismic random noise with fuzzy GK clustering, Journal of Geophysics and Engineering, 5, 457-468. Hashemi, H. and Beukelaar, P., 2017, Clustering seismic datasets for optimized facies analysis using a SSCSOM technique, 79th EAGE Conference and Exhibition, Paris, France, 12-15 June, https://doi.org/10.3997/2214-4609. 201700916. Khemchandani, R., Pah, A. and Chandra, S., 2016, Fuzzy least squares twin support vector clustering, Neural comput. and Applic, 10.1007/s00521-016-2468-4. Liu, L., Liu, Y. J. and Tong, Sh., 2019, Fuzzy-Based Multierror Constraint Control for Switched Nonlinear Systems and Its Applications, IEEE Transactions on Fuzzy Systems, 27(8), 1519-1531. Mirzakhanian, M., Khoshdel, H., Asnaashar, A. and Sokooti, R., 2015, Reservoir Discrimination Using EEI Analysis, 77th EAGE conference and Exhibition, Madrid, Spain. Nikravesh, M., Zadeh, L.A. and Aminzadeh, F., 2003, Soft computing and intelligent data analysis in oil exploration, Elsevier. Sharifi, J., Hafezi Moghadam, N., Lashkaripour, G., R., Javaherian, A. and Mirzakhanian, M., 2019, Application of Extended Elastic Impedance in Seismic Geomechanics, Geophysics, 84(3), 429- 446. Sharifi, J. and Mirzakhanian, M., 2019, Full-angle extended elastic impedance, Interpretation, 7 (4), T869-T885. Wang, W. and Zheng, Y., 2007, On Fuzzy Cluster Validity Indices, Fuzzy Sets and Systems, 158, 2095-2117. Wang, D., Zheng M., Li, J., Li, Z., Li, J. Q., Song C. and Chen, X., 2017, Intelligent constellation diagram analyzer using convolutional neural network-based deep learning, Optics Express, 25, 17150–17166, https://doi.org/10 .1364/OE.25.017150. Whitcombe, D. N., 2002, Elastic impedance normalization, Geophysics, 67, 60-62. Whitcombe, D. N., Connolly, P. A., Reagan, R. L. and Redshaw, T. C., 2002, Extended elastic impedance for fluid and lithology prediction, Geophysics, 67(1), 63-67. Wrona, T., Pan, I., Gawthorpe, R.L. and Fossen, H., 2018, Seismic facies analysis using machine learning, Geophysics, 83(5), 83-95. Yenwongfai, H. D., Mondol, N. H., Faleide, J. I. and Lecomte, I., 2017, Prestack simultaneous inversion to predict lithology and pore fluid in the Realgrunnen Subgroup of the Goliat Field, southwestern Barents Sea, Interpretation, 5(2), 75–96, https://doi.org/10.1190/INT-2016-0109.1. Zarei, M. and Hashemi, H., 2019, Edge Detector Radon Transform for Seismic Multiple Attenuation-Tunisia-25 – 28 November. Zhao, T., Jayaram, V., Roy, A. and Marfurt, K. J., 2015, A comparison of classification techniques for seismic facies recognition, Interpretation, 3(4), 29-58. Zoeppritz, K., 1919, Erdbebenwellen VIII B, Über die Reflexion und Durchgang seismischer Wellen durch Unstetigkeitsflächen, Gottinger Nachr., 1, 66–84. | ||
|
آمار تعداد مشاهده مقاله: 389 تعداد دریافت فایل اصل مقاله: 288 |
||