Register      Login
Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

Initialising reservoir models for history matching using pre-production 3D seismic data: constraining methods and uncertainties

Mohammad Emami Niri 1 3 David E. Lumley 1 2
+ Author Affiliations
- Author Affiliations

1 School of Earth and Environment, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.

2 School of Physics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.

3 Corresponding author. Email: mohammad1199@gmail.com

Exploration Geophysics 48(1) 37-48 https://doi.org/10.1071/EG15013
Submitted: 14 February 2015  Accepted: 30 August 2015   Published: 1 October 2015

Abstract

Integration of 3D and time-lapse 4D seismic data into reservoir modelling and history matching processes poses a significant challenge due to the frequent mismatch between the initial reservoir model, the true reservoir geology, and the pre-production (baseline) seismic data. A fundamental step of a reservoir characterisation and performance study is the preconditioning of the initial reservoir model to equally honour both the geological knowledge and seismic data. In this paper we analyse the issues that have a significant impact on the (mis)match of the initial reservoir model with well logs and inverted 3D seismic data. These issues include the constraining methods for reservoir lithofacies modelling, the sensitivity of the results to the presence of realistic resolution and noise in the seismic data, the geostatistical modelling parameters, and the uncertainties associated with quantitative incorporation of inverted seismic data in reservoir lithofacies modelling. We demonstrate that in a geostatistical lithofacies simulation process, seismic constraining methods based on seismic litho-probability curves and seismic litho-probability cubes yield the best match to the reference model, even when realistic resolution and noise is included in the dataset. In addition, our analyses show that quantitative incorporation of inverted 3D seismic data in static reservoir modelling carries a range of uncertainties and should be cautiously applied in order to minimise the risk of misinterpretation. These uncertainties are due to the limited vertical resolution of the seismic data compared to the scale of the geological heterogeneities, the fundamental instability of the inverse problem, and the non-unique elastic properties of different lithofacies types.

Key words: 3D and time-lapse 4D seismic, elastic properties, geostatistics, lithofacies modelling, seismic inversion, uncertainty analysis.


References

Aki, K., and Richards, P., 1980, Quantitative seismology - theory and methods: W. H. Freeman and Company.

Araktingi, U., and Bashore, W., 1992, Effects of properties in seismic data on reservoir characterization and consequent fluid-flow predictions when integrated with well logs: SPE Annual Technical Conference and Exhibition, SPE-24752-MS.

Avseth, P., Mukerji, T., and Mavko, G., 2005, Quantitative seismic interpretation: applying rock physics tools to reduce interpretation risk: Cambridge University Press.

Behrens, R., MacLeod, M., Tran, T., and Alimi, A., 1998, Incorporating seismic attribute maps in 3D reservoir models: SPE Reservoir Evaluation & Engineering, 1, 122–126
Incorporating seismic attribute maps in 3D reservoir models:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXjsV2hsro%3D&md5=b3b0d4d2e32ced028e6b3a1a41e7c4e5CAS |

Bornard, R., Allo, F., Coleou, T., Freudenreich, Y., Caldwell, D., and Hamman, J., 2005, Petrophysical seismic inversion to determine more accurate and precise reservoir properties: SPE Annual Technical Conference and Exhibition, SPE 94144-MS.

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 |

Caers, J., 2005, Petroleum geostatistics: Society of Petroleum Engineers.

Caers, J., Avseth, P., and Mukerji, T., 2001, Geostatistical integration of rock physics, seismic amplitudes, and geologic models in North Sea turbidite systems: The Leading Edge, 20, 308–312
Geostatistical integration of rock physics, seismic amplitudes, and geologic models in North Sea turbidite systems:Crossref | GoogleScholarGoogle Scholar |

Coléou, T., Formento, J.-L., Gram-Jensen, M., van Wijngaarden, A.-J., Haaland, A. N., and Ona, R., 2006, Petrophysical seismic inversion applied to the troll field: 76th Annual International Meeting, SEG, Expanded Abstracts, 2107–2111.

Connolly, P., 2005, Net pay estimation from seismic attributes: 67th EAGE Conference & Exhibition, Extended Abstracts, F016.

Connolly, P., 2007, A simple, robust algorithm for seismic net pay estimation.: The Leading Edge, 26, 1278–1282
A simple, robust algorithm for seismic net pay estimation.:Crossref | GoogleScholarGoogle Scholar |

Da Veiga, S., and Le Ravalec-Dupin, M., 2010, Rebuilding existing geological models: SPE Euorope/EAGE Annual Conference, SPE 130976-MS.

Delfiner, P., and Haas, A., 2005, Over thirty years of petroleum geostatistics, in M. Bilodeau, F. Meyer, and M. Schmitt, eds., Space, structure and randomness: Springer, 89–104.

Deutsch, C. V., and Journel, A. G., 1992, GSLIB – Geostatistical software library and user’s guide: Oxford University Press.

Doyen, P. M., 1988, Porosity from seismic data: a geostatistical approach: Geophysics, 53, 1263–1275
Porosity from seismic data: a geostatistical approach:Crossref | GoogleScholarGoogle Scholar |

Doyen, P., 2007, Seismic reservoir characterization: an earth modelling perspective: EAGE Publications.

Dubrule, O., 2003, Geostatistics for seismic data integration in earth models: EAGE Publications.

Duda, R., Hart, P., and Stork, D., 2001, Pattern classification: John Wiley and Sons.

Dutton, S. P., Flanders, W. A., and Barton, M. D., 2003, Reservoir characterization of a Permian deep-water sandstone, East Ford field, Delaware basin, Texas: AAPG Bulletin, 87, 609–627
Reservoir characterization of a Permian deep-water sandstone, East Ford field, Delaware basin, Texas:Crossref | GoogleScholarGoogle Scholar |

Eaton, T. T., 2006, On the importance of geological heterogeneity for flow simulation: Sedimentary Geology, 184, 187–201
On the importance of geological heterogeneity for flow simulation:Crossref | GoogleScholarGoogle Scholar |

Emami Niri, M., and D. Lumley, 2014, Probabilistic reservoir property modeling jointly constrained by 3D seismic data and hydraulic unit analysis: SPE Asia Pacific Oil and Gas Conference and Exhibition, Society of Petroleum Engineers, SPE-171444-MS.

Emami Niri, M., and Lumley, D., 2015, Simultaneous optimization of multiple objective functions for reservoir modeling: Geophysics, 80, M53–M67
Simultaneous optimization of multiple objective functions for reservoir modeling:Crossref | GoogleScholarGoogle Scholar |

Falcone, G., Gosselin, O., Maire, F., Marrauld, J., and Zhakupov, M., 2004, Petroelastic modelling as key element of 4D history matching– a field example: SPE Annual Technical Conference and Exhibition, SPE 90466-MS.

Fatti, J. L., Smith, G. C., Vail, P. J., Strauss, P. J., and Levitt, P. R., 1994, Detection of gas in sandstone reservoirs using AVO analysis: a 3-D seismic case history using the Geostack technique: Geophysics, 59, 1362–1376
Detection of gas in sandstone reservoirs using AVO analysis: a 3-D seismic case history using the Geostack technique:Crossref | GoogleScholarGoogle Scholar |

Francis, A., 2010, Limitations of deterministic seismic inversion data as input for reservoir model conditioning: 80th Annual International Meeting, SEG, Expanded Abstracts, 2396–2400.

Gassmann, F., 1951, Über die elastizität poröser medien: Vierteljahrsschrift der Naturforschenden Gesellschaft in Zürich, 96, 1–23

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 |

Hampson, D., Russell, B., and Bankhead, B., 2005, Simultaneous inversion of pre-stack seismic data: 75th Annual International Meeting, SEG, Extended Abstracts, 1633–1637.

Isaaks, E. H., and Srivastava, R. M., 1989, An introduction to applied geostatistics: Oxford University Press.

Journel, A., and Gomez-Hernandez, J., 1993, Stochastic imaging of the Wilmington clastic sequence: SPE Formation Evaluation, 8, 33–40
Stochastic imaging of the Wilmington clastic sequence:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3sXitFKjtro%3D&md5=a3156b24d67e6a0ebcaf82f1ff465fa5CAS |

Kemper, M., 2010, Rock physics driven inversion: the importance of workflow: First Break, 28, 69–81

Lancaster, S., and Whitcombe, D., 2000, Fast-track ‘coloured’ inversion: 70th SEG Annual Conference, Expanded Abstracts, 1572–1575.

Le Ravalec-Dupin, M., Enchery, G., Baroni, A., and Da Veiga, S., 2011, Preselection of reservoir models from a geostatistics-based petrophysical seismic inversion: SPE Reservoir Evaluation & Engineering, 14, 612–620
Preselection of reservoir models from a geostatistics-based petrophysical seismic inversion:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtl2ju7fL&md5=bf5925b7860c95b266c8cdccb332fd84CAS |

Lindseth, R. O., 1979, Synthetic sonic logs - a process for stratigraphic interpretation.: Geophysics, 44, 3–26
Synthetic sonic logs - a process for stratigraphic interpretation.:Crossref | GoogleScholarGoogle Scholar |

Lumley, D., 1995, Seismic time-lapse monitoring of subsurface fluid flow: Ph.D. thesis, Stanford University.

Lumley, D., and Behrens, R., 1998, Practical issues of 4D seismic reservoir monitoring: what an engineer needs to know: SPE Reservoir Evaluation & Engineering, 1, 528–538
Practical issues of 4D seismic reservoir monitoring: what an engineer needs to know:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXisVyhsQ%3D%3D&md5=f13c8715ef0895b87212842a154b80a5CAS |

Mavko, G., Mukerji, T., and Dvorkin, J., 2009, The rock physics handbook: tools for seismic analysis of porous media: Cambridge University Press.

Menezes, C., and Gosselin, O., 2006, From logs scale to reservoir scale: upscaling of the petro-elastic model: SPE Europe/EAGE Annual Conference, Paper SPE 100233-MS.

Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., and González, E. F., 2001, Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization: The Leading Edge, 20, 313–319
Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization:Crossref | GoogleScholarGoogle Scholar |

Nivlet, P., Lucet, N., Tonellot, T., Albouy, E., Bunge, G., Doligez, B., Roggero, F., Lefeuvre, F., Piazza, J., and Brechet, E., 2005, Facies analysis from pre-stack inversion results in a deep offshore turbidite environment: 75th Annual International Meeting, SEG, Expanded Abstracts, 1323–1326.

Nivlet, P., Lefeuvre, F., and Piazza, J., 2007, 3D seismic constraint definition in deep-offshore turbidite reservoir: Oil & Gas Science and Technology- Revue d’IFP, 62, 249–264
3D seismic constraint definition in deep-offshore turbidite reservoir:Crossref | GoogleScholarGoogle Scholar |

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 |

Oldenburg, D. W., Scheuer, T., and Levy, S., 1983, Recovery of the acoustic impedance from reflection seismograms: Geophysics, 48, 1318–1337
Recovery of the acoustic impedance from reflection seismograms:Crossref | GoogleScholarGoogle Scholar |

Pyrcz, M. J., and Deutsch, C. V., 2014, Geostatistical reservoir modelling: Oxford University Press.

Reading, H. G., 2009, Sedimentary environments: processes, facies and stratigraphy: John Wiley & Sons.

Rossini, C., Brega, F., Piro, L., Rovellini, M., and Spotti, G., 1994, Combined geostatistical and dynamic simulations for developing a reservoir management strategy: a case history: Journal of Petroleum Technology, 46, 979–985
Combined geostatistical and dynamic simulations for developing a reservoir management strategy: a case history:Crossref | GoogleScholarGoogle Scholar |

Russell, B., and Hampson, D., 1991, Comparison of poststack seismic inversion methods: Technical Program, SEG, Expanded Abstract, 876–878.

Sams, M., and Saussus, D., 2010, Uncertainties in the quantitative interpretation of lithology probability volumes: The Leading Edge, 29, 576–583
Uncertainties in the quantitative interpretation of lithology probability volumes:Crossref | GoogleScholarGoogle Scholar |

Saussus, D., and Sams, M., 2012, Facies as the key to using seismic inversion for modelling reservoir properties: First Break, 30, 45–52

Sen, M. K., 2006, Seismic inversion: Society of Petroleum Engineers.

Whitcombe, D. N., Connolly, P. A., Reagan, R. L., and Redshaw, T. C., 2002, Extended elastic impedance for fluid and lithology prediction.: Geophysics, 67, 63–67
Extended elastic impedance for fluid and lithology prediction.:Crossref | GoogleScholarGoogle Scholar |

Wood, A. B., 1955, A textbook of sound: being an account of the physics of vibrations with special reference to recent theoretical and technical developments: MacMillan.

Zakrevsky, K., 2011, Geological 3D modelling: EAGE Publications.