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Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin, India

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Abstract

Well log analysis provides the information on petrophysical properties of reservoir rock and its fluid content. The present study depicts interpretation of well log responses such as gamma ray, resistivity, density and neutron logs from six wells, namely W-1, W-2, W-9, W-12, W-13 and W-14 under the study area of Krishna-Godavari (K-G) basin. The logs have been used primarily for identification of lithology and hydrocarbon-bearing zones. The gamma ray log trend indicates deposition of cleaning upward sediment. Coarsening upward, clayey-silty-sandy bodies have been evidenced from the gamma ray log. Gas-bearing zones are characterised by low gamma ray, high deep resistivity and crossover between neutron and density logs. Total 14 numbers of hydrocarbon-bearing zones are identified from wells W-9, W-12, W-13 and W-14 using conventional log analysis. Crossplotting techniques are adopted for identification of lithology and fluid type using log responses. Crossplots, namely P-impedance vs. S-impedance, P-impedance vs. ratio of P-wave and S-wave velocities (Vp/Vs) and lambda-mu-rho (LMR), have been analysed to discriminate between lithology and fluid types. Vp/Vs vs. P-impedance crossplot is able to detect gas sand, brine sand and shale whereas P-impedance vs. S-impedance crossplot detects shale and sand trends only. LMR technique, i.e. λρ vs. μρ crossplot is able to discriminate gas sand, brine sand, carbonate and shale. The LMR crossplot improves the detectability and sensitivity of fluid types and carbonate lithology over other crossplotting techniques. Petrophysical parameters like volume of shale, effective porosity and water saturation in the hydrocarbon-bearing zones in these wells range from 5 to 37%, from 11 to 36 and from 10 to 50% respectively. The estimated petrophysical parameters and lithology are validated with limited core samples and cutting samples from five wells under the study area.

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References

  • Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146(1):54–62

    Article  Google Scholar 

  • Asquith, G. and Gibson, C., 1983, Basic well log analysis for geologists. AAPG methods in exploration series, Tulsa, pp.216

  • Avseth P, Mukerji T, Mavko G (2005) Quantitative seismic interpretation: applying rock physics to reduce interpretation risk. Cambridge University Press, Cambridge, U.K.

    Book  Google Scholar 

  • Bastia R, Nayak P (2006) Tectonostratigraphy and depositional patterns in Krishna offshore basin, Bay of Bengal. Lead Edge 25:839–845

    Article  Google Scholar 

  • Bateman RM (1985) Openhole log analysis and formation evaluation. Prentice Hall PTR, New Jersey, p 647

    Google Scholar 

  • Chow JJ, Li M-C, Fuh S (2005) Geophysical well log study on the paleo environment of the hydrocarbon producing zones in the Erchungchi Formation, Hsinyin, SW Taiwan. TAO 16:531–543

    Google Scholar 

  • Das B, Chatterjee R (2017) Wellbore stability analysis and prediction of minimum mudweight for few wells in Krishna-Godavari Basin, India. Int J Rock Mech Min Sci 93:30–37

    Google Scholar 

  • Das B, Chatterjee R, Singha DK, Kumar R (2017) Post-stack seismic inversion and attribute analysis in shallow offshore of Krishna-Godavari basin, India. J Geol Soc India 90:32–40

    Article  Google Scholar 

  • Datta Gupta S, Chatterjee R, Farooqui MY (2012) Rock physics template (RPT) analysis of well logs and seismic data for lithology and fluid classification in Cambay basin. Int J Earth Sci 101(5):1407–1426

    Article  Google Scholar 

  • Eichkitz CG, Schreilechner MG, Amtmann J, Schmid C (2009) Shallow seismic reflection study of the Gschliefgraben landslide deposition area—interpretation and three dimensional modeling. Aust J Earth Sci 102:52–60

    Google Scholar 

  • Ejedawe JE, Coker SJL, Lambert-Aikhionbare DO, Alofe KB, Adoh FO (1984) Evolution of oil-generative window and oil and gas occurrence in Tertiary Niger Delta Basin. AAPG Bull 68:1744–1751

    Google Scholar 

  • Fertl, W. and Frost, E., 1980, Evaluation of shaly clastic reservoir rocks. Journal of Petroleum Technology, September, pp. 1641-1645

  • Gong C, Wang Y, Steel RJ, Olariu C, Xu Q, Liu X, Zhao Q (2015) Growth styles of shelf-margin clinoforms: prediction of sand- and sediment-budget partitioning into and across the shelf. J Sediment Res 85:209–229

    Article  Google Scholar 

  • Goodway, B., Chen T. and Downtown, J 1997, Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters, 67th Annual international Meeting, SEG, Expanded Abstracts, 183–186

  • Goodway B, Perez M, Varsek J, Abaco C (2010) Seismic Petrophysics and isotropic-anisotropic AVO methods for unconventional gas exploration. Lead Edge 29:1500–1508

    Article  Google Scholar 

  • Hasan MM, Akter F, Deb PK (2013) Formation characterization and identification of potential hydrocarbon zone for titas gas field, Bangladesh using wireline log data. Int J Sci Eng Res 4(5):1512–1518

    Google Scholar 

  • Inichinbia S, Sule PO, Ahmed AL, Hamaza H, Lawal KM (2014) Petro physical analysis of among hydrocarbon field fluid and lithofacies using well log data. J Appl Geol Geophysics 2:86–96

    Article  Google Scholar 

  • Jipa CD (2012) Coarsening upward sedimentation in the Middle Pontian Dacian Basin, prograding shoreline or delta front? Geo-Eco-Marina 18:45–64

    Google Scholar 

  • Kumar R, Das B, Chatterjee R, Sain K (2016) A methodology of porosity estimation from inversion of post-stack seismic data. Jf Nat Sci Eng 28:356–364

    Article  Google Scholar 

  • Leonard R (1983) Geology and hydrocarbon accumulations, Columbus Basin, offshore Trinidad. AAPG Bull 67:1081–1093

    Google Scholar 

  • Manmohan M, Rao MRR, Kamraju AVVS, Yalamarty Y (2003) Origin and occurrence of Lower Cretaceous high gamma-high resistivity (Raghavapuram) shale—a key stratigraphic sequence for hydrocarbon exploration in Krishna-Godavari Basin, A.P. J Geol Soc India 62:271–289

    Google Scholar 

  • Masoudi P, Tokhmechi B, Zahedi A, Jafari MA (2011) Developing a method for identification of net zones using log data and diffusivity equation. J Mining Environ 2(1):53–60

    Google Scholar 

  • Mukerji T, Avseth P, Mavko G, Takahashi I, González EF (2001) Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization. Lead Edge 20(3):313–319

    Article  Google Scholar 

  • Omudu, LM., Ebeniro, JO., Xynogalas, M., Osayande, N. and Olotu, S., 2008, Fluid discrimination and reservoir characterization from onshore Niger Delta, Society of Exploration Geophysicists, Annual General Meeting, Las Vegas, 2001–2005

  • Opara AI (2010) Prospectivity evaluation of ussol field, onshore Niger Delta Basin, using 3-D seismic and well log data. Pet Coal 52:307–315

    Google Scholar 

  • Pendrel, J. and Riel, P V., 2000, Estimation and interpretation of P and S impedance volumes from simultaneous inversion of P-wave offset seismic data, Society of Exploration Geophysicists, Expanded Abstracts, 1–4

  • Popielski, A C., Heidari, Z., Verdin, C T., 2012, Rock classification from conventional well logs in hydrocarbon-bearing shale, SPE 159255, October, San Antonio

  • Poupon, A. and Leveaux, J., 1971, Evaluation of water saturation in shaly formations, Trans. SPWLA 12thAnnual Logging Symposium, pp.1–2

  • Quirein, J., Witkowsky, J., Truax, J. A., Galford, J. E., Spain, D. S. and Odumosu, T., 2010, Integrating core data and wireline geochemical data for formation evaluation and characterization of shale-gas reservoirs, SPE annual technical conference and exhibition, Paper id SPE-134559

  • Rao GN (2001) Sedimentation, stratigraphy, and petroleum potential of Krishna-Godavari basin, East Coast of India. AAPG Bull 85:1623–1643

    Google Scholar 

  • Rider, M H., 1999, Geologic interpretation of well logs. Whittles Publishing Services

  • Rider, M H., 2002, The geological interpretation of well logs, 2nd edition. Rider-French Consulting Ltd, pp.280

  • Samantaray, S. and Gupta, P., 2008, An interpretative approach for gas zone identification and lithology discrimination using derivatives of λ∗ρ and μ∗ρ attributes, 7th Biennial International Conference and Exposition on Petroleum Geophysics, Society of Petroleum Geophysicists, Hyderabad, 386–341

  • Sarasty, J J. and Stewart, R R., 2003, Analysis of well-log data from the white rose oilfield, offshore Newfoundland, CREWES Research Report, 15

  • Schlumberger, 1972, The essentials of log interpretation practice, Services Techniques Schlumberger, pp. 58

  • Selley, R C., 1998, Elements of petroleum geology. Department of Geology, Imperial College, London, 37-145

  • Seni SJ, Desselle BA, Standen A (1994) Scope and construction of a gas and oil atlas series of the Gulf of Mexico: examples from Texas offshore lower Miocene plays. Gulf Coast Assoc Geol Soci Trans 44:681–690

    Google Scholar 

  • Shrivastava, C., Ganguly, S. and Khan, Z., 2008, Reconstructing sedimentary depositional environment with borehole imaging and core: a case study from eastern offshore India, International Petroleum Technology Conference, Kuala Lumpur, December

  • Singha D, Chatterjee R (2017) Rock physics modeling in sand reservoir through well log analysis, Krishna-Godavari basin, India. Geomechanics Eng 13(1):99–117

    Google Scholar 

  • Weber KJ (1987) Hydrocarbon distribution patterns in Nigerian growth fault structures controlled by structural style and stratigraphy. J Pet Sci Eng 1:91–104

    Article  Google Scholar 

Download references

Acknowledgements

Authors are thankful to ONGC and GSPCL for their kind approval for sharing of well log data. Data are available for academic purposes.

Funding

The work is funded by Ministry of Earth Science through the R&D project MoES /P.O./(Seismo)/1(138) 2011 dated 9 November 2012.

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Authors and Affiliations

  1. Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines), Dhanbad, Dhanbad, 826004, India

    Baisakhi Das & Rima Chatterjee

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  1. Baisakhi Das
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  2. Rima Chatterjee
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Correspondence to Rima Chatterjee.

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Das, B., Chatterjee, R. Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin, India. Arab J Geosci 11, 231 (2018). https://doi.org/10.1007/s12517-018-3587-2

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