Estimation of Brittleness Index Using Post-Stack Inversion of Seismic Data: Example from Perth Basin in Western Australia

Document Type : Research Article


1 M.Sc. Student, Department of Geology, Faculty of Science, Urmia University, Urmia, Iran

2 Associate Professor, Department of Geology, Faculty of Science, Urmia University, Urmia, Iran

3 Associate Professor, Department of Geology, Faculty of Natural Science, Tabriz University, Tabriz, Iran


Brittleness is one of the most important properties of the rock. Brittleness is a function of strength and indicates rock strength to deformation in the elastic modules. However, there is no direct and standard method for brittleness measurement but it can be done indirectly by using rock properties such as different ratios of compressive strength and tensile strength of rock to determine the concept of brittleness. The purpose of this study is to investigate the concepts of brittleness which are presented by the researchers and to use seismic inversion, multi-attribute analysis and neural network in the Whicher-Range field in Perth, Western Australia to estimate brittleness. The Perth sedimentary basin stretches about 100,000 square kilometers in the north-south direction of the western Australian margin. About half of this sedimentary basin is located 1 km deep in the sea. Whicher-Range gas field is 22 km south of Baselton and 200 km south of Perth. Two wells and one seismic section of Whicher-Range field are selected in this research. The lowest brittleness indexes in the first and second wells drilled 1 km apart, are 1.69 and 1.67 MPa using criteria. The highest values of the brittleness are 39.78 and 48.15 MPa, respectively, which are difficult for drilling. The starting point of the inversion process is to have post-stack seismic data, velocity model, well logs, and seismic horizons. The product of the density log and sonic velocity is equal to the impedance. Then the current impedance is converted from depth to time using the appropriate depth to time relation. As a result, the convolution of a suitable wavelet and reflectivity over time will produce the synthetic seismic trace. Adaptation rate between synthetic seismic trace and composite field seismic trace yields an acceptable result. Next, the initial modeling is performed using wavelet, geological model, and a model-based algorithm. Next, the acoustic impedance of the inversion process along with other attributes is used to construct the optimal combination of attributes to estimate the brittleness index. First, an attribute is selected that has the highest correlation with the target log and the least estimation error in the training step. Then the second attribute, which makes the best combination with the first attribute, has the lowest estimation error. Then each attribute step is added to its previous step combination until the resulting combination results in the lowest estimation error. The results based on this method are obtained by increasing the number of attributes and decreasing the estimation error, while the error in the validation stage until the optimal attribute combination, is ascending. In the next step, three types of neural network algorithm including probabilistic method, multi-layer feed forward and radial basis function are used to estimate the target parameter, with optimal combination of available attributes and the use of neural network algorithm training from the optimal attributes using the Hampson-Russell software. In the last step, multi-attribute analysis is compared with three neural network algorithms. The results indicate a higher correlation coefficient for probabilistic neural network than that of multi-attribute analysis for determination of the brittleness index.


Main Subjects

جعفری، م.، 1395، تخمین پارامترهای مخزنی از تلفیق وارون‌سازی لرزه­ای و تحلیل چند نشانگری با استفاده از شبکة عصبی در یکی از میادین نفتی در جنوب ایران، پایان­نامه کارشناسی‌ارشد، دانشگاه ارومیه، ایران.
سراج‌امانی، م.، نیکروز، ر. و کدخدائی، ع.، 1398، تخمین پارامترهای مکانیک سنگ با استفاده از نشانگرهای لرزه­ای و شبکة عصبی در سازند سوء حوضة پرت واقع در استرالیا غربی، اولین همایش ملی پردازش سیگنال و تصویر در ژئوفیزیک، دانشگاه صنعتی شاهرود، ایران.
کدخدائی ایلخچی، ر.، 1393، سرشت نمایی مخزنی ماسه­های گازی سفت (کم‌تراوا) میدان ویچررنج در حوضة پرت واقع در استرالیای غربی، رساله دکتری، دانشگاه فردوسی مشهد، ایران.
کدخدائی ایلخچی، ع.، 1397، ارزیابی سازندهای نفت دار، مؤسسه انتشارات دایره دانش.
کدخدائی ایلخچی، ع.، 1388، تخمین پارامترهای ژئوشیمیایی و پتروفیزیکی از نمودارهای چاه‌پیمایی و نشانگرهای لرزه­ای با استفاده از سیستم‌های هوشمند در میادین هیدروکربنی جنوب ایران، رساله دکتری، دانشگاه تهران، ایران.
نیکروز، ر.، ثیاب قدسی، ع.ا. و حسنعلی زاده، پ.، 1396، شناسایی تله­های چینه­ای سازند سروک با استفاده از لاگ­های پتروفیزیکی و نشانگرهای لرزه­ای در یکی از میادین نفتی جنوب غرب ایران، م. فیزیک زمین و فضا، 1، 53-69.
وطن‌خواه، ح.، 1396، بررسی تأثیر شکنندگی بر قابلیت برش نمونه­های سنگی، پایان­نامه کارشناسی‌ارشد، دانشگاه تربیت مدرس، ایران.
Altindag, R., 2010, Assessment of some brittleness indexes in rock-drilling efficiency. Rock mechanics and rock engineering, 43(3), 361-370.
Altindag, R., 2003, Correlation of specific energy with rock brittleness concepts on rock cutting. Journal of the Southern African Institute of Mining and Metallurgy, 103(3), 163-171.
Andreev, G. E., 1995, Brittle failure of rock materials: test results and constitutive models, A. A. Balkema/Rotterdam, 446.
Crostella, A. and Backhouse, J., 2000, Geology and petroleum exploration of the central and southern Perth Basin, Western Australia (No. 57). Perth, WA: Geological Survey of Western Australia.
Draper, N. R. and Smith, H., 1998, Applied regression analysis (Vol. 326). John Wiley & Sons.
Göktan, R. M., 1991, Brittleness and micro-scale rock cutting efficiency. Mining Science and Technology, 13(3), 237-241.
Hall, P. B. and Kneale, R. L., 1992, Perth Basin rejuvenated. The APPEA Journal, 32(1), 33-43.
Hetenyi, M., 1966, Handbook of experimental stress analysis, Wiley, New York, 15.
Hucka, V. and Das, B., 1974, October. Brittleness determination of rocks by different methods. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 11(10), 389-392. Pergamon.
Jafari, M., Nikrouz, R. and Kadkhodaie, A., 2017, Estimation of acoustic-impedance model by using model-based seismic inversion on the Ghar Member of Asmari Formation in an oil field in southwestern Iran. The Leading Edge, 36(6), 487-492.
Jones, D. O. and Ellis, G., 2000, Atlas of petroleum fields, onshore Perth Basin, Petroleum Division, DMEWA, 1, 122.
Lawn, B. R. and Marshall, D. B., 1979, Hardness, toughness, and brittleness: an indentation analysis. Journal of the American ceramic society, 62(7‐8), 347-350.
Lindseth, R. O., 1979, Synthetic sonic logs—A process for stratigraphic interpretation. Geophysics, 44(1), 3-26.
McNally, G. H., 1987, Estimation of coal measures rock strength using sonic and neutron logs. Geoexploration, 24(4-5), 381-395.
Meng, F., Zhou, H., Zhang, C., Xu, R. and Lu, J., 2015, Evaluation methodology of brittleness of rock based on post-peak stress–strain curves. Rock Mechanics and Rock Engineering, 48(5), 1787-1805.
Morley, A., 1954, Strength of materials, 11th ed. Longmans, Green, London, 532.
Obert, L. and Duvall, W. I., 1967, Rock mechanics and the design of structures in rock (No. BOOK). J. Wiley.
Özfırat, M. K., Yenice, H., Şimşir, F. and Yaralı, O., 2016, A new approach to rock brittleness and its usability at prediction of drillability. Journal of African Earth Sciences, 119, 94-101.
Ramsay, J. G., 1967, Folding and fracturing of rocks. McGraw Hill Book Company, 568.
Russell, B. H., 2004, The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes.
Russell, B.H., Lines, L. R. and Hampson, D. P., 2003, Application of the radial basis function neural network to the prediction of log properties from seismic attributes. Exploration Geophysics, 34(2), 15-23.
Sharifzadeh, A., 2008, Tight gas resources in the Northern Perth Basin, Petroleum W.A Magazine, 41-44.
Sharifzadeh, A., 2007, Tight gas resources in Western Australia, Petroleum W.A Magazine, 28-31.
Yagiz, S., 2009, Assessment of brittleness using rock strength and density with punch penetration test. Tunnelling and Underground Space Technology, 24(1), 66-74.
Yagiz, S. and Gokceoglu, C., 2010, Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Systems with Applications, 37(3), 2265-2272.
Yilmaz, N. G., Karaca, Z., Goktan, R. M. and Akal, C., 2009, Relative brittleness characterization of some selected granitic building stones: influence of mineral grain size. Construction and Building Materials, 23(1), 370-375.