Artificial neural network (ANN)–based workflow has been applied to the multi-channel seismic reflection data from the Krishna-Godavari (K-G) basin on the Eastern continental margin of India for delineation of prominent structural features such as chimneys and faults. To eliminate the noise and enhance the data quality, the seismic data is conditioned and filtered by data-conditioning techniques such as dip-steered and structural filters. A single attribute is not sufficient for the delineation of structures such as faults and chimneys; hence, an amalgamation of multiple attributes is necessary for the enhancement of laterally continuous seismic events. Hence, multi-attributes such as dip variance, curvature, coherency, energy, similarity, instantaneous frequency, and frequency washout ratio are extracted from the conditioned data which were combined and fed as inputs for the ANN for the chimney and fault detection. The neural network is trained at the identified chimney/non-chimney and fault/non-fault locations, which generates a final output, namely chimney probability attribute and a fault probability attribute, revealing an improved visibility of faults and chimneys in the seismic data. This multi-attribute approach shows more reliability in comparison with individual attribute responses, which helps in better structural interpretation. The study presents a workflow for better visualization of the subsurface structural features which thereby helps in detailed structural interpretation of the seismic data.
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Al-Dossary S, and Marfurt KJ (2006) 3-D volumetric multi-spectral estimates of reflector curvature and rotation: Geophysics, 71, P41-P51.
Aminzadeh F, de Groot PF (2004) Soft computing for qualitative and quantitative seismic object and reservoir property prediction. Part 1: neural network applications. First Break 22:49–54
Bahorich M, Farmer S (1995) 3-D seismic discontinuity for faults and stratigraphic features: the coherence cube. Lead Edge 14:1053–1058
Bastia R, (2007) Geologic Settings and Petroleum Systems of India’s East Coast Offshore Basins: Concepts and Applications: Eastern Book Corporation, 204p.
Chopra S (2002) Coherence cube and beyond. EAGE First Break 20:27–33
Chopra S, and Marfurt KJ (2007) Seismic attributes for prospect identification and reservoir characterization: SEG.
Collett, T. S., Riedel, M. H., Cochran, J. R., Boswell, R., Presley, J., Kumar, P., Sibal, V., (2008) NGHP expedition 01 Scientists, 2008. National Gas Hydrate Program Expedition 01 initial Reports. New Delhi: Directorate General of Hydrocarbons.
Collett TS, Boswell R, Cochran JR, Kumar P, Lall M, Mazumdar A, Ramana MV, Ramprasad T, Riedel M, Sain K, Sathe AV, Vishwanath K, the NGHP Expedition 01 Scientific Party (2014) Geologic implications of gas hydrates in the Indian offshore: results of the National Gas Hydrate Program Expedition 01. J Mar Petrol Geol 58:3–28
Connolly DL (2015) Visualization of vertical hydrocarbon migration in seismic data: case studies from the. Dutch North Sea: Interpretation 3:SX21–SX27
Dewangan P, Ramprasad T, Ramana MV, Mazumdar A, Desa M, Badesab FK (2010) Seabed morphology and gas venting features in the continental slope region of Krishna-Godavari basin, Bay of Bengal: implications in gas-hydrate exploration. Mar Pet Geol 27(7):1628–1641. https://doi.org/10.1016/j.marpetgeo.2010.03.015
de Rooij M, Tingdahl K (2002) Meta-attributes - the key to multivolume, multi attribute interpretation. Lead Edge 2002:1050–1053
Heggland R et al (1998) Gas seepage as an indicator of deeper prospective reservoirs. A study based on exploration 3D seismic data. Mar Pet Geol 15:1–9
Heggland R, Meldahl P, Bril AH and de Groot PFM (1999) The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: part II; interpretation, SEG 69th Annual Meeting, Houston, Oct. 31 - Nov. 5, Expanded Abstracts Vol. 1, pp 935- 937.
Heggland R et al (2000) Chimneys in the Gulf of Mexico. The American Oil and Gas Reporter 43:78–83
Heggland R (1997) Detection of gas migration from a deep source by the use of exploration 3D seismic data. Mar Geol 137:41–47
Kluesner JW, Brothers DS (2016) Seismic attribute detection of faults and fluid pathways within an active strike-slip shear zone: new insights from high-resolution 3D P-Cable seismic data along the Hosgri Fault, offshore. California: Interpretation 4:SB131–SB148
Kumar P, Collett TS, Boswell R, Cochran JR, Lall M, Mazumdar A, Ramana MV, Ramprasad T, Riedel M, Sain K, Sathe AV, Vishwanath K, Yadav US, the NGHP Expedition 01 Scientific Party (2014) Geologic implications of gas hydrates in the Indian offshore: Krishna-Godavari Basin, Mahanadi Basin, Andaman Sea, Kerala-Konkan Basin. J Mar Petrol Geol 58:29–98
Kumar PC (2016) Application of geometric attributes for interpreting faults from seismic data: an example from Taranaki basin, New Zealand: 86th Annual International Meeting, SEG. Expanded Abstracts 2077-2081
Kumar PC, Mandal A (2017) Enhancement of fault interpretation using multi-attribute analysis and artificial neural networks (ANN) approach: a case study from Taranaki Basin. New Zealand, Exploration Geophysics 49:409–424. https://doi.org/10.1071/EG16072
Lee M, Collett T (2009) Gas hydrate saturations estimated from fractured reservoir at Site NGHP-01-10, Krishna-Godavari Basin, India. J Geophys Res 114(B7):B07102
Luo Y, Marhoon M, AlDossary S, Alfaraj M (2002) Edge-preserving smoothing and applications. Lead Edge 21:136–158
Marfurt KJ, Sudhaker V, Gersztenkorn A, Crawford KD, Nissen SE (1999) Coherency calculations in the presence of structural dip. Geophysics 64:104–111
Meldahl, P., Heggland, R., Bril, B., and de Groot, P., 1999, The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: part I; methodology: 69th Annual international Meeting, SEG, Expanded Abstracts, 931 - 934.
Meldahl P, Heggland R, Bril B, de Groot P (2001) Identifying fault and gas chimneys using multi-attributes and neural networks. Lead Edge 20:474–482
Poulton M (2002) Neural networks as an intelligence amplification tool: a review of applications. Geophy. 67(3):979–993
Prabhakar KN, Zutshi PL (1993) Evolution of southern part of Indian east coast basins. Journal of the Geological Society of lndia 41:215–230
Ramana MV, Ramprasad T, Maria D (2001) Seafloor spreading magnetic anomalies in the Enderby basin, East Antarctica. Earth Planet Sci Lett 191:241–255
Ramesh R, Subramanian V (1988) Temporal, spatial and size variation in the sediment transport in the Krishna River basin, India. J Hydrol 98:53–65
Rao GN (2001) Sedimentation, stratigraphy, and petroleum potential of Krishna–Godavari basin, east coast of India. Am Assoc Pet Geol Bull 85(9):1623–1643
Roberts A (2001) Curvature attributes and their application to 3-D interpreted horizons. First Break 19:85–100
Sain K, Gupta HK (2008) Gas hydrates: Indian scenario. J Geol Soc India 72:299–311
Sain K, Gupta HK (2012a) Gas hydrates in India: potential and development. Gondwana Res 22(2):645–657
Sain K, Gupta HK (2012b) Gas-hydrates in Krishna-Godavari and Mahanadi Basins. J Geol Soc India 79:553–556
Subrahmanyam D, Rao PH (2008) Seismic attributes - a review. Hyderabad International Conference and Exposition on Petroleum Geology 398-403
Tingdahl KM, Bril AH, de Groot PF (2001) Improving seismic chimney detection using directional attributes: journal of petroleum. Sci Eng 29:205–211
Tingdahl KM (2003) Improving seismic chimney detection using directional attributes. In: Nikravesh M, Zadeh L, Aminazadeh F (eds) Developments in petroleum sciences. Elsevier, Amsterdam, pp 157–173
Tingdahl KM, de Rooij M (2005) Semi-automatic detection of faults in 3D seismic data. Geophys Prospect 53(4):533–542
Zhang J-H, Liu Z, Zhu B-H, Feng D-Y, Zhang M-Z, Zhang X-F (2011) Fluvial reservoir characterization and identification: a case study from Laohekou Oilfield. Appl Geophys 8(3):81–88. https://doi.org/10.1007/s11770-011-0288-y
The authors thank the Editor and the anonymous reviewer for their kind suggestions in improving the manuscript. Director, CSIR-National Geophysical Research Institute is acknowledged for providing data of the K-G basin. Director, Wadia Institute of Himalayan Geology, Dehradun is thanked for according permission to publish this work. Special thanks to dGB Earth Sciences for providing the OpendTect software license for academic use at the Centre for Earth Ocean and Atmospheric Sciences (CEOAS), University of Hyderabad, India. The authors are thankful to the Head, CEOAS, for providing the facilities for carrying out this work.
Conflict of interest
The authors declare that they have no competing interests.
Responsible Editor: Narasimman Sundararajan
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Ramu, C., Sunkara, S.L., Ramu, R. et al. An ANN-based identification of geological features using multi-attributes: a case study from Krishna-Godavari basin, India. Arab J Geosci 14, 299 (2021). https://doi.org/10.1007/s12517-021-06652-z
- Artificial neural networks
- Seismic attributes
- Gas chimney
- Structural features