Estimation of Acoustic Impedance of the Tight Sandstones Using Seismic Inversion Methods: A Case Study from Whicher-Range Gas Field in the Perth Basin, Australia

Document Type : Research Paper

Authors

1 Department of Geology, Faculty of Sciences, Urmia University, Iran

2 Earth Science Department, Faculty of Natural Science, Tabriz University, Iran

Abstract

Acoustic impedance can be considered as a measure of rock strength to the propagation of waves, which is a product of the density and compressive wave velocity of the rocks. Geophysicists are trying to estimate the acoustic impedance of rock layers from reflective seismic data, called seismic inversion. The difference of acoustic impedance in the common layer of layers reflects the seismic waves. Acoustic impedance is the property of rock itself, and the harder the rocks are, the higher the acoustic impedance is. Seismic inversion can be used as a reliable method for (1) estimating reservoir properties quantitatively and (2) obtaining quantified values of acoustic impedance from seismic data, well-logging data, and interpreted horizons. In general, acoustic impedance has a strong relationship with petrophysical properties such as fracture, saturation and porosity. The purpose of this study was to estimate the initial model of acoustic impedance using different seismic inversion methods to determine reservoir characteristics and compare their results in the Whicher-Range field of Perth Basin, Western Australia. For this purpose, two wells and a seismic section from the field were used. All steps were performed using the Hampson-Russell software. First, the depth-time relationship between well logs and seismic data was corrected. Then, the compatibility of the synthetic seismogram resulting from the seismic wavelet convolution with the series of reflections produced at the well location was investigated with seismic data. Various inversion algorithms including Model-based, Band-limited, Sparse Spike (Linear Programming) and Sparse Spike (Maximum Likelihood) were inspected. The correlation coefficient in all methods shows an acceptable value. Using wavelet extraction and investigation of different inversion algorithms, the model-based method, as the best method with high correlation coefficient and less error among other algorithms, was used for initial modeling. In the interval of 2050 to 2250 milliseconds of the studied section, acoustic impedance decreases significantly, indicating porosity in the reservoir area.
 

Keywords


[1]. Gholami A, Ansari HR (2017) Estimation of porosity from seismic attributes using a committee model with bat-inspired optimization algorithm, Journal of Petroleum Science and Engineering, 152: 238-249.##
[2]. Soleimani F, Hosseini E, Hajivand F (2020) Estimation of reservoir porosity using analysis of seismic attributes in an Iranian oil field, Journal of Petroleum Exploration and Production Technology, 10, 4: 1289-1316. ##
[3]. Maurya SP, Singh KH, Kumar A, Singh NP (2018) Reservoir characterization using post-stack seismic inversion techniques based on real coded genetic algorithm, Journal of Geophysics, 39, 2: 95-103. ##
[4]. Maurya SP, Singh KH, Singh NP (2019) Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada, Marine Geophysical Research, 40, 1:51-71. ##
[5]. Kushwaha PK, Maurya SP, Singh NP, Rai P (2019) Estimating subsurface petro-physical properties from raw and conditioned seismic reflection data: A comparative study, 285-306. ##
[6]. Somasundaram S, Mund B, Soni R, Sharda R (2017) Seismic attribute analysis for fracture detection and porosity prediction: A case study from tight volcanic reservoirs, Barmer Basin, India, The Leading Edge, 36, 11: 947b1-947b7. ##
[7]. رحیمی م.، "برگردان داده‌های لرزه‌ای سه بعدی به امپدانس صوتی و کاربرد آن در تخمین تخلخل (میدان گازی گنبدلی)،" پایان‌نامه کارشناسی ارشد، دانشگاه تهران، ایران، 1381. ##
[8]. کدخدائی ایلخچی ع.، "تخمین پارامترهای ژئوشیمیایی و پتروفیزیکی از نمودارهای چاه پیمایی و نشانگرهای لرزه‌ای با استفاده از سیستم‌های هوشمند در میادین هیدروکربنی جنوب ایران،" رساله دکتری، دانشگاه تهران، ایران، 1388. ##
[9]. Latimer RB, Davidson R, Van Riel P (2000) An interpreter's guide to understanding and working with seismic-derived acoustic impedance data, The leading edge, 19, 3: 242-256. ##
[10]. کدخدائی ایلخچی ر.، "سرشت نمایی مخزنی ماسه‌های گازی سفت (کم تراوا) میدان ویچررنج در حوضه پرت واقع در استرالیای غربی،" رساله دکتری، دانشگاه فردوسی مشهد، ایران، 1393. ##
[11]. Pouryousefy E, Johnson L, Ghasemiziarani M (2016) Simulation uncertainties in tight gas reservoirs; case-study on whicher range field in Western Australia, International Journal of Current Research, 8: 35367-35374. ##
[12]. Crostella A, Backhouse J (2000) Geology and petroleum exploration of the central and southern Perth Basin, Western Australia, 57, Perth, WA: Geological Survey of Western Australia. ##
[13]. Kadkhodaie-Ilkhchi R, Kadkhodaie A, Rezaee R, Mehdipour V (2019) Unraveling the reservoir heterogeneity of the tight gas sandstones using the porosity conditioned facies modeling in the Whicher Range field, Perth Basin, Western Australia, Journal of Petroleum Science and Engineering, 176: 97-115. ##
[14]. Kadkhodaie-Ilkhchi R, Rezaee R, Moussavi-Harami R, Kadkhodaie-Ilkhchi A (2013) Analysis of the reservoir electrofacies in the framework of hydraulic flow units in the Whicher Range Field, Perth Basin, Western Australia, Journal Petroleum Science and Engineering, 111: 106-120. ##
[15]. Hall PB, Kneale RL (1992) Perth Basin rejuvenated, The APPEA Journal, 32, 1: 33-43. ##
[16]. Playford PE, Cockbain AE, Low GH (1988) Geology of the Perth basin, south-western Australia and their geological implications. Review of Palaeobotany and Palynology, 65, 1-4: 229-237. ##
[17]. Russell BH (1988), Introduction to seismic inversion methods, Society of Exploration Geophysicists.
[18]. نیکنام چنارستان سفلی ر.، "تخمین نمودارهای پتروفیزیکی با استفاده از وارون‌سازی داده‌های لرزه‌ای،" پایان‌نامه کارشناسی ارشد، دانشگاه شهید چمران اهواز، ایران، 1393. ##
[19]. وکیلی آ.، خلیلی س. ط.، حسینی س.ک.، موسوی حرمی س. ر. و چهرازی، ع.، "مقایسه نتایج حاصل از وارون‌سازی داده‌های لرزه‌ای دو بعدی میدان نفتی هندیجان و بهرگانسر به روش‌های مختلف،" مجله پژوهش نفت، دوره 25، شماره 82، صفحه 32-44، 1394. ##
[20]. جعفری م.، "تخمین پارامترهای مخزنی از تلفیق وارون سازی لرزه‌ای و تحلیل چند نشانگری با استفاده از شبکه عصبی در یکی از میادین نفتی در جنوب ایران،" پایان‌نامه کارشناسی ارشد، دانشگاه ارومیه، ایران، 1395. ##
[21]. سراج امانی م.، نیکروز ر.، کدخدائی ع.، "تخمین پارامترهای مکانیک سنگ با استفاده از نشانگرهای لرزه‌ای و شبکه عصبی در سازند سوء حوضه پرت واقع در استرالیا غربی،" اولین همایش ملی پردازش سیگنال و تصویر در ژئوفیزیک، دانشگاه صنعتی شاهرود، ایران، 1398. ##
[22]. فرج‌پور ز.، نبی بیدهندی م.، ترابی م.ر.، "برگردان داده‌های لرزه‌ای سه بعدی به امپدانس صوتی در یکی از میادین نفتی جنوب غربی ایران،" فصلنامه زمین، سال چهارم، شماره 3، صفحه 53-66، 1388. ##
[23]. Cooke DA, Schneider WA (1983) Generalized linear inversion of reflection seismic data, Geophysics, 48, 6: 665-676. ##
[24]. Mallick S (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm, Geophysics, 60, 4: 939-954. ##
[25]. Maurya SP, Singh KH (2017) Band limited impedance inversion of Blackfoot field, Alberta, Canada, Joural of Geophysics, 38, 1: 57-61. ##
[26]. Russell B, Hampson D (2006) The old and the new in seismic inversion, CSEG Recorder, 31, 10: 5-11. ##
[27]. Oldenburg DW, Scheuer T, Levy S (1983) Recovery of the acoustic impedance from reflection seismograms, Geophysics, 48, 10: 1318-1337. ##
[28]. Levy S, Fullagar PK (1981) Reconstruction of a sparse spike train from a portion of its spectrum and application to high-resolution deconvolution, Geophysics, 46, 9: 1235-1243. ##
[29]. Maurya SP, Singh NP (2018), Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution-A case study from the Blackfoot field, Canada, Journal of Applied Geophysics, 159: 511-521. ##