Abstract
Reservoir characterization is necessary to compute reservoir parameters for hydrocarbon potential and production optimization. The limitation of robust data and the presence of cultural noise is a constraint for reservoir characterization in the Raniganj basin located in India. Based on available well logs and two-dimensional post-stack seismic data, a model-based seismic inversion is executed to generate acoustic impedance by converting acoustic reflectivity into rock elastic parameters. Moreover, the seismic attributes obtained from the inversion are implemented in neural network architectures to map shale volume, Young’s modulus, and Poisson’s ratio. Error analysis between predicted and actual results demonstrate multi-layered feed-forward or probabilistic neural network display a better result in obtaining reservoir parameters. The mapped reservoir section shows the acoustic impedance varying from 5000 to 16,000 (g/cc)*(m/s), shale volume ranging from 15% to 55%, Young’s modulus, and Poisson’s ratio vary from 0.5–9.5 GPa and 0.23–0.27 respectively. Cross-plot between Young’s modulus versus Poisson’s ratio classifies lithology from brittleness and it increases with depth. Neural network architectures help to identify the best model in delineating shale barriers for designing hydraulic fracturing treatments. Results from this study have added significant values in engineering application and will help in ongoing coalbed methane exploration and future geomechanical studies. However, limitations exist in resolving thin coal seams as the seismic resolution depends on the wavelength, velocity, and frequency of waves in the formation.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Alabi, A. and Enikanselu, A.P. (2019) Integrating seismic acoustic impedance inversion and attributes for reservoir analysis over ‘DJ’ field, Niger Delta. Jour. Petrol. Explor. Prod. Tech., v.9, pp.2487–2496. doi:https://doi.org/10.1007/s13202-019-0720-z
Austin, O., Onyekuru, S.O., Ebuka, O.A. and Abdulrazzaq, T.Z. (2018) Application of model-based inversion technique in a field in the coastal swamp depo belt, Niger delta. Internat. Jour. Advan. Geosci., v.6(1), pp.122–126.
Banerjee, A., and Chatterjee, R. (2021a) A Methodology to Estimate Proximate and Gas Content Saturation with Lithological Classification in Coalbed Methane Reservoir, Bokaro Field, India. Natural Resour. Res., v.30, pp.2413–2429. doi:https://doi.org/10.1007/s11053-021-09828-2.
Banerjee, A., and Chatterjee, R. (2021b) Fracture analysis using Stoneley wave in coalbed methane reservoir. Near Surface Geophysics. doi:https://doi.org/10.1002/nsg.12176.
Banerjee, A., and Chatterjee, R. (2022) Pore pressure modeling and in situ stress determination in Raniganj basin, India. Bull. Engg. Geol. Environ., v.81, 49. doi:https://doi.org/10.1007/s10064-021-02502-0
Bateman, R.M. (1985) Openhole Log Analysis and Formation Evaluation. Pretice Hall PTR, New Jersey, 647p.
Boonen, P. (2003) Advantages and challenges of using logging-while-drilling data in rock mechanical log analysis and wellbore stability modeling. In: Proceedings AADE National Technology Conference, Texas; 1–3 April 2003.
Chatterjee, R., Paul, S., and Pal, P.K. (2019) Relation between coalbed permeability and in-situ stress magnitude for coalbed methane exploration in Jharia and Raniganj coalfields, India. The Leading Edge, pp.800–807. https://doi.org/10.1190/tle38100800.1
Coal Atlas of India (1993) Central Mine Planning and Design Institute Ranchi. Coal India.
Ghosh, S.C. (2002) The Raniganj Coal Basin: an example of an Indian Gondwana rift. Sediment. Geol., v.147, pp.155–176. doi:https://doi.org/10.1016/S0037-0738(01)00195-6
Grana, D. and Della Rossa, E. (2010) Probabilistic petrophysical properties estimation integrating statistical rock physics with seismic inversion. Geophysics, v.75, pp.O21–O37. doi:https://doi.org/10.1190/1.3386676
Gogoi, T. and Chatterjee, R. (2018) Estimation of petro-physical parameters using seismic inversion and neural network modeling in Upper Assam basin, India. Geoscience Frontiers, v.10, pp.1113–1124. doi:https://doi.org/10.1016/j.gsf.2018.07.002
Hampson, D. and Russell, B. (1985) Maximum-likelihood seismic inversion. Geophysics, v.50(8), pp.1380–1381.
Hampson, D., Schuelke, J. and Quirein, J. (2001) Use of multi-attribute transforms to predict log properties from seismic data. Geophysics, v.66, pp.220–236.
Hatampour, A., Schaffie, M. and Jafari, S. (2016) Estimation of NMR total and free fluid porosity from seismic attributes using intelligent systems: a case study from an Iranian carbonate gas reservoir. Arabian Jour. Sci. Engg., v.42, pp.315–326.
Lavergne, M., and Willim, C. (1997) Inversion of seismogram and pseudo velocity logs. Geophys. Prospect., v.25, pp.231–250. doi:https://doi.org/10.1111/j.1365-2478.1977.tb01165.x
Leiphart, D., and Hart, B.S. (2001). Comparison of linear regression and a probabilistic neural network to predict porosity from 3-d seismic attributes in lower brushy canyon channeled sandstones, Southeast New Mexico. Geophysics, v.66, pp.1349–1358.
Maity, D., and Aminzadeh, F. (2012) Reservoir characterization of an unconventional reservoir by integrating micro-seismic, seismic, and well log data. In: SPE western regional meeting, 21–23 March, 2012, Bakersfield, California, USA: Society of Petroleum Engineers. doi:https://doi.org/10.2118/154339-MS
Mallick, S. (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics, v.60(4), pp.939–954.
Mondal, S., Yadav, Y., and Chatterjee, R. (2020) Rock physics forward modeling to predict seismic behavior: A case study for exploration target in Mahanadi basin, east coast of India. Geophys. Prospect., v.68, pp.2186–2194. doi:https://doi.org/10.1111/1365-2478.12983.
Mohebali, B., Tahmassebi, A., Mayer-Baese, A. and Gandomi, A.K. (2020) Probabilistic neural networks: a brief overview of theory, implementation, and application. Handbook of Probabilistic Models, pp.347–367. doi:https://doi.org/10.1016/B978-0-12-816514-0.00014-X.
Mondal, S., Chatterjee, R. and Chakraborty, S. (2021). An integrated approach for reservoir characterization in deep-water Krishna-Godavari basin, India: A Case study. Jour. Geophys. Engg., v.18, pp.134–144.
Saadu, Y.K. and Nwankwo, C.N. (2018) Petrophysical evaluation and volumetric estimation within Central swamp depobelt, Niger Delta, using 3-D seismic and well logs. Egyptian Jour. Petrol., v.27, pp.531–539. doi:https://doi.org/10.1016/j.ejpe.2017.08.004
Sacchi, M.D. and Ulrych, T.J. (1996) Bayesian regularization of some seismic operators. Maximum entropy and Bayesian methods. In: Hanson, K.M. and Silver, R.N. (Eds.), Kluwer Academic Publishers, v.79, pp.425–436.
Saggaf, M.M., Toksoz, M.N. and Mustafa, H.M. (2003) Estimation of Reservoir Properties from Seismic Data by Smooth Neural Networks. Geophysics, v.68, pp.1969–1983. doi:https://doi.org/10.1190/1.1635051
Sarana, S. and Kar, R. (2011) Effect of igneous intrusive on coal microconstituents: Study from an Indian Gondwana coalfield. Internat. Jour. Coal Geol., v.85(1), pp.161–167
Shahraeeni, M.S. and Curtis, A. (2011) Fast probabilistic non-linear petrophysical inversion. Geophysics, v.76, pp.E45–E58.
Tan, P., Jin, Y., Yuan, L. et al., (2019) Understanding hydraulic fracture propagation behaviour in tight sandstone-coal interbedded formations: an experimental investigation. Petroleum Sci., v.16, pp.148–160. doi:https://doi.org/10.1007/s12182-018-0297-z.
Acknowledgments
The authors are thankful to Oil and Natural Gas Corporation Limited, India for providing data, and supporting us to conduct the research work. The authors are also grateful to Mr. A. K Dwivedi, Ex-Director (Exploration), ONGC, Mr. N. C. Pandey, Ex-Director (T&FS), ONGC, Mr. Aditya Johri (Asset Manager) ONGC Bokaro and Prof. Rajiv Shekhar (Director, IIT-ISM, Dhanbad, India) for the support and encouragement for the research work. Authors acknowledge financial support from SERB/IMP/2018/000369 project.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Banerjee, A., Chatterjee, R. Mapping of Reservoir Properties using Model-based Seismic Inversion and Neural Network Architecture in Raniganj Basin, India. J Geol Soc India 98, 479–486 (2022). https://doi.org/10.1007/s12594-022-2005-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12594-022-2005-2