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Gas chimney and hydrocarbon detection using combined BBO and artificial neural network with hybrid seismic attributes

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Abstract

The exact interpretation of structure of the Earth is essential to avoid drilling of dry holes or locating hydrocarbon reservoirs in faulty regions. The economic approaches are required to locate the reservoirs without damaging the tectonic plates of the Earth. The identification of gas chimneys provides an impressive approach to indirectly interpret the hydrocarbon reservoirs as the chimneys form the migration pathway for gas from reservoirs, and it looks like a gas cloud in the seismic data. The proposed method using biogeography-based optimization (BBO) with supervised neural networks enables the effective interpretation of chimneys and hydrocarbons. Seismic attributes are measured on the pre-processed seismic data, where the attributes play an important role in providing qualitative information on structure of the Earth. Continuity, instantaneous and amplitude attributes are measured to provide the details on the areas of discontinuity, bright-spot regions with high amplitude and low frequency. The calculated attributes are picked from selected locations in the pre-processed seismic data, and the hybrid combination of attributes enables effective, economic chimney identification and provides indications to locate the reservoirs. The redundant and highly correlated features are eliminated using BBO which in turn improves the classification accuracy. BBO algorithm retains the best solution in each generation, and the fitness of the algorithm is verified with supervised neural networks. The proposed algorithm is applied on F3 block dataset, and BBO selects 20 predominant features among 75 features. The optimal solution after 50 generations enables the classification of chimneys through multilayer feed-forward supervised neural network. The results indicate that the chimney classification accuracy is improved by 90%, and the mean square error is minimized with BBO as a feature selection algorithm compared to other evolutionary algorithms.

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References

  • Abdizadeh H, Kadkhodaie A, Ahmadi A, Heidarifard MH (2017) Estimation of total organic carbon from well logs and seismic sections via neural network and ant colony optimization approach: a case study from the Mansuri oil field, SW Iran. Geopersia 7(2):255–266

    Google Scholar 

  • Alvarado JM, Aminzadeh F, Connolly DL (2003) Application of gas chimney technology in the Lamprea Area, Offshore GOM. In: SEG technical program expanded abstracts 2003. Society of Exploration Geophysicists, pp 2334–2334

  • Alzaki TT (2016) 2D seismic data and gas chimney interpretation in the South Taranaki Graben, New Zealand

  • Aminzadeh F, Connolly D (2002) Looking for gas chimneys and faults. AAPG Explor 23(12):20–21

    Google Scholar 

  • Aminzadeh F, De Groot P (2006) Neural networks and other soft computing techniques with applications in the oil industry. Eage Publications, The Hague

    Google Scholar 

  • An P, Moon WM (1993) Reservoir characterization using feedforward neural networks. In: SEG technical program expanded abstracts 1993. Society of Exploration Geophysicists, pp 258–262

  • Brown AR (1996) Seismic attributes and their classification. Lead Edge 15(10):1090–1090

    Article  Google Scholar 

  • Castagna JP, Sun S, Siegfried RW (2003) Instantaneous spectral analysis: detection of low-frequency shadows associated with hydrocarbons. Lead Edge 22(2):120–127

    Article  Google Scholar 

  • Chen Q, Sidney S (1997) Seismic attribute technology for reservoir forecasting and monitoring. Lead Edge 16(5):445–448

    Article  Google Scholar 

  • Chopra S, Marfurt KJ (2005) Seismic attributes—a historical perspective. Geophysics 70(5):3SO–28SO

    Article  Google Scholar 

  • Chopra S, Marfurt K (2006) Seismic attributes—a promising aid for geologic prediction. CSEG Rec 31:110–120

    Google Scholar 

  • Ebrahimi P, Razavi SM, Ismaeili S, Azarpour M (2013) Gas-chimney detection in 3D seismic by neural network. Pet Sci Technol 31(11):1188–1195

    Article  Google Scholar 

  • Foschi M, Cartwright JA, Peel FJ (2014) Vertical anomaly clusters: evidence for vertical gas migration across multilayered sealing sequences. AAPG Bull 98(9):1859–1884

    Article  Google Scholar 

  • Hammond AL (1974) Bright spot: better seismological indicators of gas and oil. Science 185(4150):515–517

    Article  Google Scholar 

  • Hashemi H, Tax DMJ, Duin RPW, Javaherian A, Groot PD (2008) Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier. Nonlinear Process Geophys 15(6):863–871

    Article  Google Scholar 

  • Heggland R (1997) Detection of gas migration from a deep source by the use of exploration 3D seismic data. Mar Geol 137(1–2):41–47

    Article  Google Scholar 

  • Heggland R (2002) Seismic evidence of vertical fluid migration through faults: applications of chimney and fault detection. In: Proceedings from AAPG Hedburg conference, Vancouver

  • Heggland R (2004) Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis. AAPG Bull 88(6):857–868

    Article  Google Scholar 

  • Heggland R, Meldahl P, Bril B, de Groot P (1999) The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: part II; interpretation. In: SEG technical program expanded abstracts 1999. Society of Exploration Geophysicists, pp 935–940

  • Korneev VA, Goloshubin GM, Daley TM, Silin DB (2004) Seismic low-frequency effects in monitoring fluid-saturated reservoirs. Geophysics 69(2):522–532

    Article  Google Scholar 

  • Li X, Yin M (2013) Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Trans Nanobiosci 12(4):343–353

    Article  Google Scholar 

  • Ligtenberg JH, Thomsen RO (2003) Fluid migration path detection and its application to basin modelling. In: 65th EAGE conference & exhibition

  • Liu B, Tian M, Zhang C, Li X (2015) Discrete biogeography based optimization for feature selection in molecular signatures. Mol Inform 34(4):197–215

    Article  Google Scholar 

  • Løseth H, Gading M, Wensaas L (2009) Hydrocarbon leakage interpreted on seismic data. Mar Pet Geol 26(7):1304–1319

    Article  Google Scholar 

  • Luo Y, Higgs WG, Kowalik WS (1996) Edge detection and stratigraphic analysis using 3D seismic data. In: SEG technical program expanded abstracts. Society of Exploration Geophysicists, pp 324–327

  • Meldahl P, Heggland R, Bril B, 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. In: SEG technical program expanded abstracts. Society of Exploration Geophysicists, pp 931–934

  • Meldahl P, Heggland R, Bril B, de Groot P (2001) Identifying faults and gas chimneys using multiattributes and neural networks. Lead Edge 20(5):474–482

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209

    Article  MathSciNet  Google Scholar 

  • Nourollah H, Keetley J, O’Brien G (2010) Gas chimney identification through seismic attribute analysis in the Gippsland Basin, Australia. Lead Edge 29(8):896–901

    Article  Google Scholar 

  • Qiang Guo Q, Nayyer Islam N, Pennington WD (2014) Tuning, AVO, and flatspot effects in North Sea Block F3. In: 2014 SEG annual meeting. Society of Exploration Geophysicists

  • Schroot BM, Schüttenhelm RTE (2003) Expressions of shallow gas in the Netherlands North Sea, Netherlands. J Geosci 82(1):91–105

    Google Scholar 

  • Sheriff RE (1991) Encyclopaedic dictionary of exploration geophysics: SEG geophysical references series 1. Society of Exploration Geophysicists (SEG), Tulsa, p 384

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Subrahmanyam D, Rao PH (2008) Seismic attributes—a review. In: 7th international conference & exposition on petroleum geophysics, Hyderabad, pp 398–404

  • Taner MT (2001) Seismic attributes. CSEG Rec 26(7):49–56

    Google Scholar 

  • Taner MT, Koehler F, Sheriff RE (1979) Complex seismic trace analysis. Geophysics 44(6):1041–1063

    Article  Google Scholar 

  • Tingdahl KM, Bril AH, de Groot PF (2001) Improving seismic chimney detection using directional attributes. J Petrol Sci Eng 29(3–4):205–211

    Article  Google Scholar 

  • Yazdani S, Shanbehzadeh J, Aminian E (2013) Feature subset selection using constrained binary/integer biogeography-based optimization. ISA Trans 52(3):383–390

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of ECE, Hindusthan College of Engineering and Technology, Coimbatore, India

    J. Ramya & P. Vijayalakshmi

  2. Department of EEE, Sri Guru Institute of Technology, Coimbatore, India

    D. Somasundareswari

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  1. J. Ramya
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  2. D. Somasundareswari
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  3. P. Vijayalakshmi
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Correspondence to J. Ramya.

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Communicated by V. Loia.

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Ramya, J., Somasundareswari, D. & Vijayalakshmi, P. Gas chimney and hydrocarbon detection using combined BBO and artificial neural network with hybrid seismic attributes. Soft Comput 24, 2341–2354 (2020). https://doi.org/10.1007/s00500-019-04064-6

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