Seismic facies-controlled prestack simultaneous inversion of elastic and petrophysical parameters for favourable reservoir prediction
Sheng Zhang 1 2 4 Handong Huang 1 2 Baoheng Zhu 1 Huijie Li 3 Lihua Zhang 3
+ Author Affiliations
- Author Affiliations
1 China University of Petroleum, State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, Beijing 102249, China.
2 Taiyuan University of Technology, College of Mining Engineering, Taiyuan 030024, China.
3 No. 1 Oil Production Plant, Petro China Huabei Oilfield Company, Renqiu 062552, China.
4 Corresponding author. Email: zhangsheng_2005@126.com
Exploration Geophysics 49(5) 655-668 https://doi.org/10.1071/EG17048
Submitted: 20 March 2017 Accepted: 23 September 2017 Published: 14 November 2017
Abstract
Comprehensive utilisation of elastic and petrophysical parameters to predict favourable reservoirs can help reduce the possibility of misidentification of resources. Existing simultaneous inversion methods for estimating the elastic and petrophysical parameters are typically based on either the Gassmann equation, with which these parameters are inverted from prestack seismic data through stochastic optimisation methods, or Wyllie’s modified equation, with which these parameters are inverted from poststack seismic data using deterministic optimisation methods. The purpose of this work is to develop a strategy for estimating the elastic and petrophysical parameters based on the Gassmann equation using deterministic prestack inversion. We employ the Gassmann equation to construct the relationship between the prestack seismic data and petrophysical parameters. We treat the joint posterior probability of elastic and petrophysical parameters as the objective function under a Bayesian framework. Given the macroscopic geological background and the poor-quality prestack seismic data, seismic facies regularisation constraints were introduced to improve the robustness and accuracy of the inversion. The very fast-simulated annealing method is used to quickly find the optimal solutions for the elastic and petrophysical parameters. Based on a model test and the application of real data demonstrates that the proposed inversion method has high accuracy and strong reliability.
Key words: elastic parameters, favourable reservoir, petrophysical parameters, seismic facies-controlled, simultaneous inversion.
References
Aki, K., and Richards, P. G., 1980, Quantitative seismology: theory and methods, vols I and II: W. H. Freeman.Alemie, W., and Sacchi, M. D., 2011, High-resolution three-term AVO inversion by means of a Trivariate Cauchy probability distribution: Geophysics, 76, R43–R55
| High-resolution three-term AVO inversion by means of a Trivariate Cauchy probability distribution:Crossref | GoogleScholarGoogle Scholar |
Bachrach, R., 2006, Joint estimation of porosity and saturation using stochastic rock-physics modeling: Geophysics, 71, O53–O63
| Joint estimation of porosity and saturation using stochastic rock-physics modeling:Crossref | GoogleScholarGoogle Scholar |
Beardsmore, G., Durrant-Whyte, H., McCalman, L., O’Callaghan, S., and Reid, A., 2016, A Bayesian inference tool for geophysical joint inversions: ASEG Extended Abstracts, 1–10.
Biot, M. A., 1941, General theory of three-dimensional consolidation: Journal of Applied Physics, 12, 155–164
| General theory of three-dimensional consolidation:Crossref | GoogleScholarGoogle Scholar |
Bosch, M., 2004, The optimization approach to lithological tomography: combining seismic data and petrophysics for porosity prediction: Geophysics, 69, 1272–1282
| The optimization approach to lithological tomography: combining seismic data and petrophysics for porosity prediction:Crossref | GoogleScholarGoogle Scholar |
Bosch, M., Cara, L., Rodrigues, J., Navarro, A, and Díaz, M, 2007, A Monte Carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes: Geophysics, 72, O29–O39
| A Monte Carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes:Crossref | GoogleScholarGoogle Scholar |
Bosch, M., Carvajal, C., Rodrigues, J., Torres, A., Aldana, M., and Sierra, J., 2009, Petrophysical seismic inversion conditioned to well-log data: methods and application to a gas reservoir: Geophysics, 74, O1–O15
| Petrophysical seismic inversion conditioned to well-log data: methods and application to a gas reservoir:Crossref | GoogleScholarGoogle Scholar |
Bosch, M., Mukerji, T., and Gonzalez, E. F, 2010, Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review: Geophysics, 75, 75A165–75A176
| Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review:Crossref | GoogleScholarGoogle Scholar |
Buland, A., and Omre, H., 2003, Bayesian linearized AVO inversion: Geophysics, 68, 185–198
| Bayesian linearized AVO inversion:Crossref | GoogleScholarGoogle Scholar |
Buland, A., Kolbjørnsen, O., Hauge, R., Skjæveland, Ø., and Duffaut, K, 2008, Bayesian lithology and fluid prediction from seismic prestack data: Geophysics, 73, C13–C21
| Bayesian lithology and fluid prediction from seismic prestack data:Crossref | GoogleScholarGoogle Scholar |
Douma, J., and Naeini, E., 2014, Application of image-guided interpolation for building LFBM prior to inversion: 76th EAGE Conference and Exhibition, Extended Abstracts, 2214–4609.
Dvorkin, J., and Nur, A., 1996, Elasticity of high-porosity sandstones: theory for two North Sea data sets: Geophysics, 61, 1363–1370
| Elasticity of high-porosity sandstones: theory for two North Sea data sets:Crossref | GoogleScholarGoogle Scholar |
Elliott, S. E., and Wiley, B. F., 1975, Compressional velocities of partially saturated, unconsolidated sands: Geophysics, 40, 949–954
| Compressional velocities of partially saturated, unconsolidated sands:Crossref | GoogleScholarGoogle Scholar |
Gassmann, F., 1951, Über die Elastizitat poroser Medien: Vierteljahrsschrift der Naturforschenden Gesellschaft, 96, 1–23
González, E. F., Mukerji, T., and Mavko, G., 2008, Seismic inversion combining rock physics and multiple-point geostatistics: Geophysics, 73, R11–R21
| Seismic inversion combining rock physics and multiple-point geostatistics:Crossref | GoogleScholarGoogle Scholar |
Grana, D., and Della Rossa, E., 2010, Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion: Geophysics, 75, O21–O37
| Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion:Crossref | GoogleScholarGoogle Scholar |
Grana, D., Mukerji, T., Dvorkin, J., and Mavko, G., 2012, Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method: Geophysics, 77, M53–M72
| Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method:Crossref | GoogleScholarGoogle Scholar |
Gunning, J., and Glinsky, M. E., 2007, Detection of reservoir quality using Bayesian seismic inversion: Geophysics, 72, R37–R49
| Detection of reservoir quality using Bayesian seismic inversion:Crossref | GoogleScholarGoogle Scholar |
Hosseinzadeh, A., 2010, Bayesian stochastic inversion (a case study from an Iranian oil field): ASEG Extended Abstracts, 1–3.
Huang, X., and Kelkar, M., 1996, Seismic inversion using heuristic combinatorial algorithm: a hybrid scheme: Proceedings of 66th International SEG Annual Conference, 1963–1966.
Huang, X., Kelkar, M., Chopra, A., and Yang, C. T., 1995, Some practical considerations for application of heuristic combinatorial algorithm to reservoir description: SEG Technical Program, Expanded Abstracts, R1025–R1027.
Huang, H., Yuan, S., Zhang, Y., Zeng, J., and Mu, W., 2016, Use of nonlinear chaos inversion inpredicting deep thin lithologic hydrocarbon reservoirs: a case study from the Tazhong oil field of the Tarim Basin, China: Geophysics, 81, B221–B234
| Use of nonlinear chaos inversion inpredicting deep thin lithologic hydrocarbon reservoirs: a case study from the Tazhong oil field of the Tarim Basin, China:Crossref | GoogleScholarGoogle Scholar |
Karimi, O., Omre, H., and Mohammadzadeh, M., 2010, Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material properties: Geophysics, 75, R1–R11
| Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material properties:Crossref | GoogleScholarGoogle Scholar |
Kemper, M., and Gunning, J., 2014, Joint impedance and facies inversion – seismic inversion redefined: First Break, 32, R89–R95
Kjønsberg, H., Hauge, R., Kolbjørnsen, O., and Buland, A, 2010, Bayesian Monte Carlo method for seismic predrill prospect assessment: Geophysics, 75, O9–O19
| Bayesian Monte Carlo method for seismic predrill prospect assessment:Crossref | GoogleScholarGoogle Scholar |
Larsen, A. L., Ulvmoen, M., Omre, H., and Buland, A, 2006, Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model: Geophysics, 71, R69–R78
| Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model:Crossref | GoogleScholarGoogle Scholar |
Lelièvre, P. G., Oldenburg, D. W., and Williams, N. C., 2009, Integrating geological and geophysical data through advanced constrained inversions: Exploration Geophysics, 40, 334–341
| Integrating geological and geophysical data through advanced constrained inversions:Crossref | GoogleScholarGoogle Scholar |
Li, J., Wei, C., Ma, X., and Yu, H., 2015, Application of sequence stratigraphy framework in improving the precision of seismic inversion: SEG Technical Program, Expanded Abstracts, R2733–R2737.
Loures, L. G., and Moraes, F. S., 2006, Porosity inference and classification of siliciclastic rocks from multiple data sets: Geophysics, 71, O65–O76
| Porosity inference and classification of siliciclastic rocks from multiple data sets:Crossref | GoogleScholarGoogle Scholar |
Mavko, G., Mukerji, T., and Dvorkin, J., 2009, The rock physics handbook: tools for seismic analysis of porous media (2nd edition): Cambridge University Press.
Murphy, W. F., 1982, Effects of microstructure and pore fluids on the acoustic properties of granular sedimentary materials: Stanford University, Department of Geophysics.
Niepsuj, M. N., 2012. Influence of the initial model on the seismic inversion result: 76th EAGE Conference and Exhibition, Extended Abstracts, 2214–4609.
Nur, A., Mavko, G., Dvorkin, J., and Galmudi, D., 1998, Critical porosity: a key to relating physical properties to porosity in rocks: The Leading Edge, 17, 357–362
| Critical porosity: a key to relating physical properties to porosity in rocks:Crossref | GoogleScholarGoogle Scholar |
Saleh, A. A., and Castagna, J. P., 2004, Revisiting the Wyllie time average equation in the case of near-spherical pores: Geophysics, 69, 45–55
| Revisiting the Wyllie time average equation in the case of near-spherical pores:Crossref | GoogleScholarGoogle Scholar |
Saltzer, R., Finn, C., and Burtz, O., 2005, Predicting V shale and porosity using cascaded seismic and rock physics inversion: The Leading Edge, 24, 732–736
| Predicting V shale and porosity using cascaded seismic and rock physics inversion:Crossref | GoogleScholarGoogle Scholar |
Sams, M., and Saussus, D., 2013, Practical implications of low frequency model selection on quantitative interpretation results: SEG Technical Program, Expanded Abstracts, 3118–3122.
Sengupta, M., and Bachrach, R., 2007, Uncertainty in seismic-based pay volume estimation: analysis using rock physics and Bayesian statistics: The Leading Edge, 26, 184–189
| Uncertainty in seismic-based pay volume estimation: analysis using rock physics and Bayesian statistics:Crossref | GoogleScholarGoogle Scholar |
Spikes, K., Mukerji, T., Dvorkin, J., and Mavko, G., 2007, Probabilistic seismic inversion based on rock-physics models: Geophysics, 72, R87–R97
| Probabilistic seismic inversion based on rock-physics models:Crossref | GoogleScholarGoogle Scholar |
Tchikaya, E. B., Chouteau, M., Keating, P., and Shamsipour, P., 2016, 3D unconstrained and geologically constrained stochastic inversion of airborne vertical gravity gradient data: Exploration Geophysics, 47, 67–84
| 3D unconstrained and geologically constrained stochastic inversion of airborne vertical gravity gradient data:Crossref | GoogleScholarGoogle Scholar |
Tian, J., Wu, G., and Zong, Z., 2013, Robust three-term AVO inversion and uncertainty analysis: Oil Geophysical Prospecting, 48, 443–449
Wijns, C., and Kowalczyk, P., 2007, Interactive geophysical inversion using qualitative geological constraints: Exploration Geophysics, 38, 208–212
| Interactive geophysical inversion using qualitative geological constraints:Crossref | GoogleScholarGoogle Scholar |
Wyllie, M. R. J., Gregory, A. R., and Gardner, G. H. F., 1958, An experimental investigation of factors affecting elastic wave velocities in porous media: Geophysics, 23, 459–493
| An experimental investigation of factors affecting elastic wave velocities in porous media:Crossref | GoogleScholarGoogle Scholar |