Improve the detection of buried channel, using Artificial Neural Networks and seismic attributes
Subject Areas : Geoscience Fields in relation with Petroleum GeologyAlireza Ghazanfari 1 , Abdolrahim Javaherian 2 * , Mojtaba Seddigh Arabani 3
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Keywords: Channel Detection Seismic Attributes Artificial Neural Network Meta Attributes,
Abstract :
Channels are one of the most important stratigraphic and morphological events. If channels place in a suitable position such as enclosed in impermeable place can make suitable oil and gas reservoir; So identifying channels are crucial. Different tools such as filters, seismic attributes, artificial neural networks, and meta-attributes have played an important role in this regard. In this paper dip-steering cube, dip-steer median filter, dip-steer diffusion filter, and fault enhancement filter, have been used. Then, various seismic attributes such as similarity, texture, spectral decomposition, energy and polar dip have been defined and studied. Therefore, work on F3 real seismic data of Dutch part of the North sea for detecting channels has been started by detecting suitable attributes. For identifying the channel in data, it has been used from compilation and combination of seismic attributes using supervised ANN (multi-layer perceptron), and development of mata-attributes, then recombine meta-attributes created along the channel, and using different interpretation point, for eliminating the impact of facies and lithology changes along the channel. Among the advantages and the reasons for using this kind of neural network (supervised), which increases the effect of the neural network and improves the result, is the ability to train the network by specifying the channel and non-channel points used in this paper. Finally, using the above methods, the identification of the channel examined in the above seismic data has been improved, and the channel has been properly detected and extracted throughout its entire length.
غضنفری بروجنی، ع.، 1395، شناسایی کانالهای مدفون با استفاده از تلفیق نشانگرهای لرزهای توسط شبکههای عصبی مصنوعی، پایاننامه کارشناسی ارشد مهندسی نفت- اکتشاف، دانشکده مهندسی نفت، دانشگاه صنعتی امیرکبیر.
Abbotts, I., 1991, United Kingdom oil and gas fields: 25 years commemorative volume: Geological Society Publishing House
Alsouki, M., Taifour, R., and Al Hamad, O., 2014, Delineating the fluvial channel system in the Upper Triassic formation of the Elward area in the Syrian Euphrates Graben using 3-D seismic attributes. Journal of Petroleum Exploration and Production Technology, 4(2), 123-132
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(3)
Aminzadeh, F. and De Groot, P., 2006, Neural networks and other soft computing techniques with applications in the oil industry. Eage Publications
Anstey, N. A., 1980, Seismic exploration for sandstone reservoirs: Springer
Barwis, J. H., McPherson, J. G., and Studlick, J. R. J., 2012, Sandstone Petroleum Reservoirs: Springer New York
Boggs, S., 2006, Principles of Sedimentology and Stratigraphy: Pearson Prentice Hall. Upper Saddle River, New Jersey
Castagna, J. P., Sun, S., and Siegfried, R. W., 2003, Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22(2), 120-127
Chakraborty, A., and Okaya, D., 1995, Frequency-time decomposition of seismic data using wavelet-based methods. Geophysics, 60(6), 1906-1916
Chopra, S., and Alexeev, V., 2006, Applications of texture attribute analysis to 3D seismic data. The Leading Edge, 25(8), 934-940
Chopra, S., and Marfurt, K. J., 2007, Seismic Attributes for Prospect Identification and Reservoir Characterization: Society of Exploration Geophysicists and European Association of Geoscientists and Engineers
De Groot, P., 2006, Interactive multi-volume seismic attribute analysis in OpendTect. Drilling & Exploration World, 15(3)
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
Hashemi, S., Javaherian, A., Ataee-pour, M., and Khoshdel, H., 2014, Two-point versus multiple-point geostatistics: the ability of geostatistical methods to capture complex geobodies and their facies associations—an application to a channelized carbonate reservoir, southwest Iran. Journal of Geophysics and Engineering, 11(6), 065002
Hashemi, S., Javaherian, A., Ataee-pour, M., Tahmasebi, P., and Khoshdel, H., 2014, Channel characterization using multiple-point geostatistics, neural network, and modern analogy: A case study from a carbonate reservoir, southwest Iran. Journal of Applied Geophysics, 111, 47-58
Liu, J., and Marfurt, K. J., 2007, Instantaneous spectral attributes to detect channels. Geophysics, 72(2), P23-P31
Love, P. L., and Simaan, M., 1984, Segmentation of stacked seismic data by the classification of image texture. In 1984 SEG Annual Meeting. Society of Exploration Geophysicists
Mathewson, J., 2008, Detection of channels in seismic images using the steerable pyramid. (M. Sc. Thesis), Colorado School of Mines
Meldahl, P., Heggland, R., Bril, B., and de Groot, P., 2001, Identifying faults and gas chimneys using multiattributes and neural networks. The Leading Edge, 20(5), 474-482
Mirkamali, M. S., Keshavarz, N., and Bakhtiari, M. R., 2016, Evolution analysis of miocene channels and faults in offshore area of Strait of Hormuz (Eastern part of Persian Gulf) using seismic meta-attributes. Journal of Petroleum Science and Engineering, 147, 116-128
Naseer, T. M., Asim, S., Ahmad, M. N., Hussain, F., and Qureshi, S. N., 2014, Application of Seismic Attributes for Delineation of Channel Geometries and Analysis of Various Aspects in Terms of Lithological and Structural Perspectives of Lower Goru Formation, Pakistan. International Journal of Geosciences, 5(12), 1490
Overeem, I., Weltje, G. J., Bishop‐Kay, C., and Kroonenberg, S. B., 2001, The Late Cenozoic Eridanos delta system in the Southern North Sea Basin: a climate signal in sediment supply?. Basin Research, 13(3), 293-312
Pang, J., 1993, Geological reservoir modelling of fluvial channel sands. UNIVERSITY OF ABERDEEN (UNITED KINGDOM)
Putnam, P. E., 1982, Fluvial channel sandstones within Upper Mannville (Albian) of Lloydminster area, Canada--geometry, petrography, and paleogeographic implications. AAPG Bulletin, 66(4), 436-459
Qiu, Y., 1987, Fluvial sandstone bodies as hydrocarbon reservoirs in lake basins
Sinha, S., Routh, P. S., Anno, P. D., and Castagna, J. P., 2005, Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, 70(6), P19-P25
Taner, M.T., 2001, Seismic attributes. CSEG recorder, 26(7): p. 48-56
Tingdahl, K. M., Bril, A. H., and de Groot, P. F., 2001, Improving seismic chimney detection using directional attributes. Journal of Petroleum Science and Engineering, 29(3), 205-211
Tingdahl, K. M. and De Rooij, M., 2005, Semi‐automatic detection of faults in 3D seismic data. Geophysical Prospecting, 53(4), 533-542
Wang, Y., 2006, Seismic time-frequency spectral decomposition by matching pursuit. Geophysics, 72(1), V13-V20
Wang, Z., Yin, C., and Zhao, W., 2011, GLCM parameters of channel texture analysis. In 2011 SEG Annual Meeting. Society of Exploration Geophysicists
Weber, K. and Eijpe, R., 1972, Permeability distribution in a Holocene distributary channel-fill near Leerdam (the Netherlands). GEOLOGIE EN MIJNBOUW (NETHERLANDS), VOL 51, NO 1, P 53-62, 1972. 14 FIG, 8 REF
West, B. P., May, S. R., Eastwood, J. E., and Rossen, C., 2002, Interactive seismic facies classification using textural attributes and neural networks. The Leading Edge, 21(10), 1042-1049
Yenugu, M., Marfurt, K. J., and Matson, S., 2010, Seismic texture analysis for reservoir prediction and characterization. The Leading Edge, 29(9), 1116-1121