Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India
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摘要
Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance(AAI) and relative acoustic impedance(RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone(TS) and Barail Arenaceous Sandstone(BAS) are the main reservoirs,delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network(MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.
Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance(AAI) and relative acoustic impedance(RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone(TS) and Barail Arenaceous Sandstone(BAS) are the main reservoirs,delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network(MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.
Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance(AAI) and relative acoustic impedance(RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone(TS) and Barail Arenaceous Sandstone(BAS) are the main reservoirs,delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network(MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.
引文
Bateman, R.M., 1985. Openhole Log Analysis and Formation Evaluation. Pretice Hall PTR, New Jersey, p. 647.
Castro de Matos, M., Penna, R., Johann, P., Marfurt, K., 2014. Relative acoustic impedance from wavelet transform. Interpretation 2(1), SA107-SA118.
Chatterjee, R., Singha, D.K., Ojha, M., Sen, M.K., Sain. K., 2016. Porosity estimation from pre-stack seismic data in gas-hydrate bearing sediments, Krishna-Godavari basin,India. Journal of Natural Gas Science and Engineering 33,562-572.
Cooke, D., O'Donnell, G., Ball, V., 1999. What is the best seismic attribute for quantitative seismic reservoir characterization?. In:SEG Technical Program Expanded Abstracts.
Das, B., Chatterjee, R., 2016. Porosity mapping from inversion of post-stack seismic data. Georesursy 18, 306-313.
Das, B., Chatterjee, R., Singha, D.K., Kumar, R., 2017. Post-stack seismic inversion and attribute analysis in shallow offshore of Krishna-Godavari basin, India. Journal of the Geological Society of India 90, 32-40.
Das, B., Chatterjee, R., 2018. Mapping of pore pressure, in-situ stress and brittleness in unconventional shale reservoir of Krishna-Godavari basin. Journal of Natural Gas Science and Engineering 50,74-89.
Evans, D., Jones, A.J., 2002. A proof of the Gamma test. Proceedings of the Royal Society of London 458, 2759-2799.
Gogoi, T., Chatterjee, R., 2017a. Mapping of shale volume using neural network modelling in part of upper Assam basin, India. In:12th Biennial International Conference&Exposition. Society of Petroleum Geophysicists. Extended Abstracts, Paper Id:P174.
Gogoi, T., Chatterjee, R., 2017b. An approach to porosity prediction from direct inversion of post stack seismic data. In:79th EAGE Conference and Exhibition2017. Extended Abstracts.
Hampson, D., Schuelke, J., Quirein, J., 2001. Use of multi-attribute transforms to predict log properties from seismic data. Geophysics 66, 220-236.
Hamada, G.M., Elshafei, M.A., 2010. Neural network prediction of porosity and permeability of heterogeneous gas sand reservoirs using NMR and conventional logs. Nafta 61(10), 451-460.
Hatampour, A., Schaffie, M., 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 Journal for Science and Engineering 42,315-326.
Ishwar, N.B., Bhardwaj, A., 2013. Petrophysical well log analysis for hydrocarbon exploration in parts of Assam arakan basin, India. In:10th Biennial International Conference&Exposition. Society of Petroleum Geophysicists, Kochi. Paper Id:P153.
Iturraran-Viveros, U., Parra, J.O., 2014. Artificial neural networks to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. Journal of Applied Geophysics 107, 45-54.
Jones, A.J., 2004. New tools in non-linear modelling and prediction. Computational Management Science 1,109-149.
Kumar, R., Das, B., Chatterjee, R., Sain, K., 2016. A methodology of porosity estimation from inversion of post stack seismic data. Journal of Natural Gas Science and Engineering 28, 356-364.
Kumar, M., Dasgupta, R., Singha, D.K., Singh, N.P., 2018. Petrophysical evaluation of well log data and rock physics modeling for characterization of Eocene reservoir in Chandmari oil field of Assam-Arakan basin, India. Journal of Petroleum Exploration and Production Technology 8(2), 323-340. https://doi.org/10.1007/s13202-017-0373-8.
Lavergne, M., Willim, C., 1977. Inversion of seismogram and pseudo velocity logs.Geophysical Prospecting 25, 231-250.
Leiphart, D.J., 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 66.1349-1358.
Lindseth, R.O., 1979. Synthetic sonic logs-a process for stratigraphic interpretation.Geophysics 44, 3-26.
Liu, Z., Liu, J., 1998. Seismic-controlled nonlinear extrapolation of well parameters using neural networks. Geophysics 63, 2035-2041.
Mallick, R.K., Raju, S.V., Mathur, N.,1997. Geochemical characterization and genesis of Eocene crude oils in a part of Upper Assam Basin, India. In:Proceedings Second International Petroleum Conference&Exhibition, PETROTECH-97, vol. 1,pp. 391-402.
Mandal, K., Dasgupta, R., 2013. Upper Assam basin and its basinal depositional history. In:10th Biennial International Conference and Exposition. Society of Petroleum Geophysicists, Kochi. Paper Id:P 292.
Mathur, N., Raju, S.V., Kulkarni.T.G., 2001. Improved identification of pay zones through integration of geochemical and log data-A case study from UpperAssam basin, India. American Association of Petroleum Geologists Bulletin 85(2), 309-323.
Maver, K.G., Rasmussen, K.B., 1995. Seismic inversion for reservoir delineation and description. In:Society of Petroleum Engineers Technical Conference and Exhibition, Bahrain, Paper Id:SPE 29798.
Murty, K.N., 1983. Geology and hydrocarbon prospects of Assam Shelf-recent advances and present status. Petroleum Asia Journal 6(4), 1-14.
Naidu, B.D., Panda, B.K., 1997. Regional source rock mapping in upper Assam Shelf.In:Proceedings of the Second International Petroleum Conference and Exhibition, Petrotech, vol. 97, pp. 350-364.
Pahari, S., Singh, H., Prasad, I.V.S.V., Singh, R.R., 2008. Petroleum Systems of Upper Assam Shelf, India. Geohorizons 14-21.
Parra, J.O., Iturraran-Viveros, U., Parra, J.S., Xu, P.C., 2015. Attenuation and velocity estimation using rock physics and neural network methods for calibrating reflection seismograms. Interpretation 3(2), SA121.
Poupon, A., Leveaux, J.,1971. Evaluation of water saturation in shaly formations. In:Trans. SPWLA 12th Annual Logging Symposium, pp. 1-2.
Rasmussen, K.B., Maver, K.G., 1996. Direct inversion for porosity of post stack seismic data. In:European 3-D Reservoir Modeling Conference. Society of Petroleum Engineers, Stavanger. SPE 35509.
Russel, B., Hampson, D., 1991. Comparison of post-stack inversion methods. In:61st SEG Annual International Meeting, Expanded Abstracts, vol. 10,pp. 876-878.
Saadu, Y.K., Nwankwo, C.N., 2018. Petrophysical evaluation and volumetric estimation within Central swamp depobelt. Niger Delta, using 3-D seismic and well logs. Egyptian Journal of Petroleum 27(4), 531-539. https://doi.org/10.1016/j.ejpe.2017.08.004.
Singh, A., Mitra, P., Srivastava, A.K., Mahapatra, C., 2015. Seismic modeling of TS-4sands embedded within Lower Clay Marker(LCM)unit and its delineation using relative acoustic impedance in Lakwa area, A&AA Basin-A case study. In:11th Biennial International Conference&Exposition. SPG India.
Singha, D.K., Chatterjee, R., Sen, M.K., Sain, K., 2014. Pore pressure prediction in gashydrate bearing sediments of Krishna-Godavari basin, India. Marine Geology357,1-11.
Wandrey, C.J., 2004. Sylhet-kopili/barail-tipam composite total petroleum System,Assam geologic province, India. U.S. Geological Survey Bulletin, 2208-D.Wyllie,M.R.J., Gregory, A.R., Gardner, G.H.F., 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics 21.40-70.
Castro de Matos, M., Penna, R., Johann, P., Marfurt, K., 2014. Relative acoustic impedance from wavelet transform. Interpretation 2(1), SA107-SA118.
Chatterjee, R., Singha, D.K., Ojha, M., Sen, M.K., Sain. K., 2016. Porosity estimation from pre-stack seismic data in gas-hydrate bearing sediments, Krishna-Godavari basin,India. Journal of Natural Gas Science and Engineering 33,562-572.
Cooke, D., O'Donnell, G., Ball, V., 1999. What is the best seismic attribute for quantitative seismic reservoir characterization?. In:SEG Technical Program Expanded Abstracts.
Das, B., Chatterjee, R., 2016. Porosity mapping from inversion of post-stack seismic data. Georesursy 18, 306-313.
Das, B., Chatterjee, R., Singha, D.K., Kumar, R., 2017. Post-stack seismic inversion and attribute analysis in shallow offshore of Krishna-Godavari basin, India. Journal of the Geological Society of India 90, 32-40.
Das, B., Chatterjee, R., 2018. Mapping of pore pressure, in-situ stress and brittleness in unconventional shale reservoir of Krishna-Godavari basin. Journal of Natural Gas Science and Engineering 50,74-89.
Evans, D., Jones, A.J., 2002. A proof of the Gamma test. Proceedings of the Royal Society of London 458, 2759-2799.
Gogoi, T., Chatterjee, R., 2017a. Mapping of shale volume using neural network modelling in part of upper Assam basin, India. In:12th Biennial International Conference&Exposition. Society of Petroleum Geophysicists. Extended Abstracts, Paper Id:P174.
Gogoi, T., Chatterjee, R., 2017b. An approach to porosity prediction from direct inversion of post stack seismic data. In:79th EAGE Conference and Exhibition2017. Extended Abstracts.
Hampson, D., Schuelke, J., Quirein, J., 2001. Use of multi-attribute transforms to predict log properties from seismic data. Geophysics 66, 220-236.
Hamada, G.M., Elshafei, M.A., 2010. Neural network prediction of porosity and permeability of heterogeneous gas sand reservoirs using NMR and conventional logs. Nafta 61(10), 451-460.
Hatampour, A., Schaffie, M., 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 Journal for Science and Engineering 42,315-326.
Ishwar, N.B., Bhardwaj, A., 2013. Petrophysical well log analysis for hydrocarbon exploration in parts of Assam arakan basin, India. In:10th Biennial International Conference&Exposition. Society of Petroleum Geophysicists, Kochi. Paper Id:P153.
Iturraran-Viveros, U., Parra, J.O., 2014. Artificial neural networks to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. Journal of Applied Geophysics 107, 45-54.
Jones, A.J., 2004. New tools in non-linear modelling and prediction. Computational Management Science 1,109-149.
Kumar, R., Das, B., Chatterjee, R., Sain, K., 2016. A methodology of porosity estimation from inversion of post stack seismic data. Journal of Natural Gas Science and Engineering 28, 356-364.
Kumar, M., Dasgupta, R., Singha, D.K., Singh, N.P., 2018. Petrophysical evaluation of well log data and rock physics modeling for characterization of Eocene reservoir in Chandmari oil field of Assam-Arakan basin, India. Journal of Petroleum Exploration and Production Technology 8(2), 323-340. https://doi.org/10.1007/s13202-017-0373-8.
Lavergne, M., Willim, C., 1977. Inversion of seismogram and pseudo velocity logs.Geophysical Prospecting 25, 231-250.
Leiphart, D.J., 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 66.1349-1358.
Lindseth, R.O., 1979. Synthetic sonic logs-a process for stratigraphic interpretation.Geophysics 44, 3-26.
Liu, Z., Liu, J., 1998. Seismic-controlled nonlinear extrapolation of well parameters using neural networks. Geophysics 63, 2035-2041.
Mallick, R.K., Raju, S.V., Mathur, N.,1997. Geochemical characterization and genesis of Eocene crude oils in a part of Upper Assam Basin, India. In:Proceedings Second International Petroleum Conference&Exhibition, PETROTECH-97, vol. 1,pp. 391-402.
Mandal, K., Dasgupta, R., 2013. Upper Assam basin and its basinal depositional history. In:10th Biennial International Conference and Exposition. Society of Petroleum Geophysicists, Kochi. Paper Id:P 292.
Mathur, N., Raju, S.V., Kulkarni.T.G., 2001. Improved identification of pay zones through integration of geochemical and log data-A case study from UpperAssam basin, India. American Association of Petroleum Geologists Bulletin 85(2), 309-323.
Maver, K.G., Rasmussen, K.B., 1995. Seismic inversion for reservoir delineation and description. In:Society of Petroleum Engineers Technical Conference and Exhibition, Bahrain, Paper Id:SPE 29798.
Murty, K.N., 1983. Geology and hydrocarbon prospects of Assam Shelf-recent advances and present status. Petroleum Asia Journal 6(4), 1-14.
Naidu, B.D., Panda, B.K., 1997. Regional source rock mapping in upper Assam Shelf.In:Proceedings of the Second International Petroleum Conference and Exhibition, Petrotech, vol. 97, pp. 350-364.
Pahari, S., Singh, H., Prasad, I.V.S.V., Singh, R.R., 2008. Petroleum Systems of Upper Assam Shelf, India. Geohorizons 14-21.
Parra, J.O., Iturraran-Viveros, U., Parra, J.S., Xu, P.C., 2015. Attenuation and velocity estimation using rock physics and neural network methods for calibrating reflection seismograms. Interpretation 3(2), SA121.
Poupon, A., Leveaux, J.,1971. Evaluation of water saturation in shaly formations. In:Trans. SPWLA 12th Annual Logging Symposium, pp. 1-2.
Rasmussen, K.B., Maver, K.G., 1996. Direct inversion for porosity of post stack seismic data. In:European 3-D Reservoir Modeling Conference. Society of Petroleum Engineers, Stavanger. SPE 35509.
Russel, B., Hampson, D., 1991. Comparison of post-stack inversion methods. In:61st SEG Annual International Meeting, Expanded Abstracts, vol. 10,pp. 876-878.
Saadu, Y.K., Nwankwo, C.N., 2018. Petrophysical evaluation and volumetric estimation within Central swamp depobelt. Niger Delta, using 3-D seismic and well logs. Egyptian Journal of Petroleum 27(4), 531-539. https://doi.org/10.1016/j.ejpe.2017.08.004.
Singh, A., Mitra, P., Srivastava, A.K., Mahapatra, C., 2015. Seismic modeling of TS-4sands embedded within Lower Clay Marker(LCM)unit and its delineation using relative acoustic impedance in Lakwa area, A&AA Basin-A case study. In:11th Biennial International Conference&Exposition. SPG India.
Singha, D.K., Chatterjee, R., Sen, M.K., Sain, K., 2014. Pore pressure prediction in gashydrate bearing sediments of Krishna-Godavari basin, India. Marine Geology357,1-11.
Wandrey, C.J., 2004. Sylhet-kopili/barail-tipam composite total petroleum System,Assam geologic province, India. U.S. Geological Survey Bulletin, 2208-D.Wyllie,M.R.J., Gregory, A.R., Gardner, G.H.F., 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics 21.40-70.