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Seismic Data Integration Workflow in Pluri-Gaussian Simulation: Application to a Heterogeneous Carbonate Reservoir in Southwestern Iran

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Abstract

In this study, we present a procedure for reservoir property modeling in a channelized carbonate reservoir based on hierarchical geostatistical simulation and seismic data integration. Because soft data integration in facies modeling has always been challenging, we used an innovative approach to incorporate seismic data in facies simulations properly. In this regard, we produced a facies proportion model (FPM) using sequential Gaussian co-simulation of facies proportion as primary data and acoustic impedance as secondary data. The facies proportions were extracted from vertical proportion curves, and the impact of seismic data in facies simulation was determined with the help of correlation coefficient maps. An alternative type of seismic-based soft data was also derived using a supervised neural network to create a facies probability cube (FPC) for each facies. Afterward, pluri-Gaussian simulation (PGS) was implemented to these two prepared soft datasets, and consequently, porosity was simulated twice in each of the models—with and without seismic-derived secondary data. Histogram analysis showed that the facies modeled with the PGS–FPM method reproduced the original well data better than the PGS–FPC method. In addition, blind wells validation showed that the PGS–FPM outputs had up to 79% accuracy, while channel geometries were better constructed using the PGS–FPC approach. The difference between the reservoir quality of channel and background was distinguishable in all hierarchical simulated porosity results. At the same time, the predicted results from simulated porosity in PGS–FPM facies had stronger correlation with true values in blind wells.

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References

  • Abdollahie-Fard, I., Braathen, A., Mokhtari, M., & Alavi, S. A. (2006). Interaction of the Zagros Fold-Thrust Belt and the Arabian-type, deepseated folds in the Abadan Plain and the Dezful Embayment, SW Iran. Petroleum Geoscience, 12, 347–362.

    Article  Google Scholar 

  • Al-Anezi, K., Kumar, S., Ebaid, A., Bonnel, A., Lucet, N., Lecante, G., & Ortet, S. (2013). Geostatistical modeling with seismic characterization of Wara/Burgan sands, Minagish Field, West Kuwait. In SPE reservoir characterization and simulation conference and exhibition, Abu Dhabi, UAE. https://doi.org/10.2118/166046-MS

  • Al-Mudhafar, W. J. (2017). Multiple-point geostatistical lithofacies simulation of fluvial sand-rich depositional environment: A case study from Zubair Formation/South Rumaila Oil Field. SPE Reservoir Evaluation & Engineering, 21, 39–53.

    Article  Google Scholar 

  • Arabpour, A., Asghari, O., & Mirnejad, H. (2019). Supergene mass-balance study assuming zero lateral copper flux using geostatistics to recognize metal source zones in exotic copper deposits. Natural Resource Research, 28, 1353–1370.

    Article  Google Scholar 

  • Armstrong, M., Galli, A., Beucher, H., Le L’och, G., Renard, D., Doligez, B., Eschard, R., & Geffroy, F. (2011). Pluri-Gaussian simulations in geosciences. Springer.

    Book  Google Scholar 

  • Armstrong, M., Lagos, T., Emery, X., Homem-de-Mello, T., Lagos, G., & Saure, D. (2021). Adaptive open-pit mining planning under geological uncertainty. Resources Policy, 72, 102086.

    Article  Google Scholar 

  • Arpat, B. G., Caers, J. K., & Haas, A. (2001). Characterization of West-Africa submarine channel reservoirs: A neural network based approach to integration of seismic data. SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana. https://doi.org/10.2118/71345-MS

    Article  Google Scholar 

  • Assadi, A., Honarmand, J., Moallemi, S. A., & Abdollahie-Fard, I. (2018). An integrated approach for identification and characterization of palaeo-exposure surfaces in the upper Sarvak Formation of Abadan Plain, SW Iran. Journal of African Earth Sciences, 145, 32–48.

    Article  Google Scholar 

  • Assadi, A., Honarmand, J., Moallemi, S. A., & Abdollahie-Fard, I. (2016). Depositional environments and sequence stratigraphy of the Sarvak Formation in an oil field in the Abadan Plain, SW Iran. Facies, 62, 26.

    Article  Google Scholar 

  • Azamifard, A., Ahmadi, M., Rashidi, F., Pourfard, M., & Dabir, B. (2020). QuiltEdge: Novel geostatistical workflow for heterogeneous reservoirs with an innovative combination of multi-variable image quilting and edge property concept to model heterogeneity and flow barriers. Journal of Petroleum Science and Engineering, 190, 107103.

    Article  Google Scholar 

  • Beucher, H., & Renard, D. (2016). Truncated Gaussian and derived methods. Comptes Rendus Geoscience, 348(7), 510–519.

    Article  Google Scholar 

  • Carter, D. C. (2003). 3-D seismic geomorphology: Insights into fluvial reservoir deposition and performance, Widuri field, Java Sea. AAPG Bulletin, 87, 909–934.

    Article  Google Scholar 

  • Chen, Q., Liu, G., Ma, X., Li, X., & He, Z. (2020). 3D stochastic modeling framework for Quaternary sediments using multiple-point statistics: A case study in Minjiang Estuary area, southeast China. Computer Geoscience, 136, 104404.

    Article  Google Scholar 

  • Chilès, J. P., & Delfiner, P. (2012). Geostatistics: Modeling spatial uncertainty (2nd ed.). Wiley.

    Book  Google Scholar 

  • Dall’Alba, V., Renard, P., Straubhaar, J., Issautier, B., Duvail, C., & Caballero, Y. (2020). 3D multiple-point statistics simulations of the Roussillon Continental Pliocene aquifer using DeeSse. Hydrology and Earth System Sciences, 24, 4997–5013.

    Article  Google Scholar 

  • de Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., & Violette, S. (2005). Dealing with spatial heterogeneity. Hydrogeology Journal, 13, 161–183.

    Article  Google Scholar 

  • de Sá, V. R., Koike, K., Goto, T., Nozaki, T., Takaya, Y., & Yamasaki, T. (2021). 3D geostatistical modeling of metal contents and lithofacies for mineralization mechanism determination of a seafloor hydrothermal deposit in the middle Okinawa Trough, Izena Hole. Ore Geology Reviews, 135, 104194.

    Article  Google Scholar 

  • Doligez, B., Hamon, Y., Barbier, M., Nader, F.H., Lerat, O., & Beucher, H. (2011). Advanced workflows for joint modelling of sedimentological facies and diagenetic properties. Impact on reservoir quality. In SPE annual technical conference and exhibition, Denver, Colorado, USA. https://doi.org/10.2118/146621-MS

  • Doyen, P. M. (2007). Seismic reservoir characterization: An earth modelling perspective. EAGE publications.

    Google Scholar 

  • Du, Y., Chen, J., Cui, Y., Xin, J., Wang, J., Li, Y., & Fu, X. (2016). Genetic mechanism and development of the unsteady Sarvak play of the Azadegan oil field, southwest of Iran. Petroleum Science, 13, 34–51.

    Article  Google Scholar 

  • Du, Y., Zhang, J., Zheng, S., Xin, J., Chen, J., & Li, Y. (2015). The rudist buildup depositional model, reservoir architecture and development strategy of the cretaceous Sarvak formation of Southwest Iran. Petroleum, 1, 16–26.

    Article  Google Scholar 

  • Dubrule, O. (2003). Geostatistics for seismic data integration in earth models. SEG and EAGE. https://doi.org/10.1190/1.9781560801962

    Book  Google Scholar 

  • Elzain, H. E., Abdullatif, O. M., Senapathi, V., Chung, S. Y., Sabarathinam, C., & Sekar, S. (2020). Lithofacies modeling of Late Jurassic in upper Ulayyah reservoir unit at central Saudi Arabia with inference of reservoir characterization. Journal of Petroleum Science and Engineering, 185, 106664.

    Article  Google Scholar 

  • Emami Niri, M., & Lumley, D. E. (2017). Initialising reservoir models for history matching using pre-production 3D seismic data: Constraining methods and uncertainties. Exploration Geophysics, 48(1), 37–48.

    Article  Google Scholar 

  • Emami Niri, M., & Lumley, D. E. (2016). Probabilistic reservoir-property modeling jointly constrained by 3D-seismic data and hydraulic-unit analysis. SPE Reservoir Evaluation & Engineering, 19(02), 253–264.

    Article  Google Scholar 

  • Emami Niri, M., & Lumley, D. E. (2015). Simultaneous optimization of multiple objective functions for reservoir modeling. Geophysics, 80(5), M53–M67.

    Article  Google Scholar 

  • Emery, X. (2007). Using the Gibbs sampler for conditional simulation of Gaussian-based random fields. Computers & Geosciences, 33(4), 522–537.

    Article  Google Scholar 

  • Emery, X., & Lantuéjoul, C. (2014). Can a training image be a substitute for a random field model? Mathematical Geosciences, 46(2), 133–147.

    Article  Google Scholar 

  • Emery, X., Ortiz, J. M., & Cáceres, A. M. (2008). Geostatistical modelling of rock type domains with spatially varying proportions: Application to a porphyry copper deposit. Journal of the South African Institute of Mining and Metallurgy, 108(5), 285–292.

    Google Scholar 

  • Emery, X., & Robles, L. N. (2009). Simulation of mineral grades with hard and soft conditioning data: Application to a porphyry copper deposit. Computational Geosciences, 13(1), 79–89.

    Article  Google Scholar 

  • Eze, P. N., Madani, N., & Adoko, A. C. (2019). Multivariate mapping of heavy metals spatial contamination in a Cu–Ni exploration field (Botswana) using turning bands co-simulation algorithm. Natural Resources Research, 28, 109–124.

    Article  Google Scholar 

  • Fang, Y., Wu, C., Guo, Z., Hou, K., Dong, L., Wang, L., & Li, L. (2015). Provenance of the southern Junggar Basin in the Jurassic: Evidence from detrital zircon geochronology and depositional environments. Sedimentary Geology, 315, 47–63.

    Article  Google Scholar 

  • Ferreira, D. J., Dutra, H. P., de Castro, T., & Lupinacci, W. M. (2021). Geological process modeling and geostatistics for facies reconstruction of presalt carbonates. Marine and Petroleum Geology, 124, 104828.

    Article  Google Scholar 

  • Haddadpour, H., & Emami Niri, M. (2021). Uncertainty assessment in reservoir performance prediction using a two-stage clustering approach: Proof of concept and field application. Journal of Petroleum Science and Engineering, 204, 108765.

    Article  Google Scholar 

  • Haque, A. E., Islam, M. A., & Shalaby, M. R. (2018). Three-Dimensional facies analysis using object-based geobody modeling: A case study for the Farewell Formation, Maui Gas Field, Taranaki Basin, New Zealand. Journal of Petroleum Exploration and Production Technology, 8, 1017–1049.

    Article  Google Scholar 

  • Hashemi, S. N., Javaherian, A., Ataee-pour, M., Tahmasebi, P., & Khoshdel, H. (2014a). Channel characterization using multiple-point geostatistics, neural network, and modern analogy: A case study from a carbonate reservoir, southwest Iran. Journal of Applied Geophysics, 111, 47–58.

    Article  Google Scholar 

  • Hashemi, S., Javaherian, A., Ataee-pour, M., & Khoshdel, H. (2014b). Two-point versus multiple point geostatistics: The ability of geostatistical methods to capture complex geobodies and their facies associations—An application to a channelized carbonate reservoir, southwest Iran. Journal of Geophysics and Engineering, 11(6), 065002.

    Article  Google Scholar 

  • Hassanzadeh Azar, J., Nabi-Bidhendi, M., Javaherian, A., & Pishvaie, M. R. (2009). Integrated seismic attributes to characterize a widely distributed carbonate clastic deposit system in Khuzestan Province, SW Iran. Journal of Geophysics and Engineering, 6, 162–171.

    Article  Google Scholar 

  • Hong, J., & Oh, S. (2021). Model selection for mineral resource assessment considering geological and grade uncertainties: Application of multiple-point geostatistics and a cluster analysis to an iron deposit. Natural Resources Research, 30, 2047–2065.

    Article  Google Scholar 

  • Hosseini, S. T., Asghari, O., & Emery, X. (2021). An enhanced direct sampling (DS) approach to model the geological domain with locally varying proportions: Application to Golgohar iron ore mine. Iran. Ore Geology Reviews, 139, 104452.

    Article  Google Scholar 

  • Hosseini, S. A., & Asghari, O. (2018). Multivariate geostatistical simulation on block-support in the presence of complex multivariate relationships: Iron ore deposit case study. Natural Resources Research, 28, 125–144.

    Article  Google Scholar 

  • Hou, W., Yang, L., Deng, D., Ye, J., Clarke, K. C., Yang, Z., Zhuang, W., Liu, J., & Huang, J. (2016). Assessing quality of urban underground spaces by coupling 3D geological models: The case study of Foshan city, South China. Computer Geoscience, 89, 1–11.

    Article  Google Scholar 

  • Høyer, A., Vignoli, G., Hansen, T. M., Vu, L. T., Keefer, D. A., & Jørgensen, F. (2017). Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies. Hydrology and Earth System Sciences, 21, 6069–6089. https://doi.org/10.5194/hess-21-6069-2017

    Article  Google Scholar 

  • Iske, A., & Randen, T. (2005). Mathematical methods and modelling in hydrocarbon exploration and production. Springer. https://doi.org/10.1007/b137702

    Book  Google Scholar 

  • Jika, H. T., Onuoha, K. M., & Dim, C. I. (2019). Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field" offshore Niger Delta Basin, Nigeria. Journal of Petroleum Exploration and Production Technology, 10, 769–781.

    Article  Google Scholar 

  • John, K., Abraham Isong, I., Michael Kebonye, N., Chapman Agyeman, P., Esther Okon, A., & Samuel Kudjo, A. (2021). Soil organic carbon prediction with terrain derivatives using geostatistics and Sequential Gaussian simulation. Journal of the Saudi Society of Agricultural Sciences, 20, 379–389.

    Article  Google Scholar 

  • Journel, A. G. (2002). Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses. Mathematical Geology, 34, 573–596.

    Article  Google Scholar 

  • Karbalaei Ramezanali, A., Feizi, F., Jafarirad, A., & Lotfi, M. H. (2019). Geochemical anomaly and mineral prospectivity mapping for vein-type copper mineralization, Kuhsiah-e-Urmak Area, Iran: Application of sequential Gaussian simulation and multivariate regression analysis. Natural Resources Research, 29, 41–70.

    Article  Google Scholar 

  • Kebonye, N. M., Eze, P. N., John, K., Gholizadeh, A., Dajcl, J., Drábek, O., Nemecek, K., & Borůvka, L. (2020). Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils. Journal of Geochemical Exploration, 222, 106680.

    Article  Google Scholar 

  • Kelishami, S. B. A., Rezaei, M., & Mohebian, R. (2022). A new approach to estimate and delineate the geothermal gradient of Iran. Geothermics, 103, 102428.

    Article  Google Scholar 

  • Krishnan, S., Boucher, A., & Journel, A. G. (2005). Evaluating information redundancy through the Tau model. In O. Leuangthong & C. V. Deutsch (Eds.), Geostatistics Banff 2004. Quantitative Geology and Geostatistics. (Vol. 14). Dordrecht: Springer. https://doi.org/10.1007/978-1-4020-3610-1_108

    Chapter  Google Scholar 

  • Lantuéjoul, C. (2002). Geostatistical simulation: Models and algorithms. Springer. https://doi.org/10.1007/978-3-662-04808-5

    Book  Google Scholar 

  • Le Blévec, T., Dubrule, O., John, C., & Hampson, G. (2020). Geostatistical Earth modeling of cyclic depositional facies and diagenesis. AAPG Bulletin, 104(3), 711–734.

    Article  Google Scholar 

  • Le Blévec, T., Dubrule, O., John, C. M., & Hampson, G. J. (2018). Geostatistical modelling of cyclic and rhythmic facies architectures. Mathematical Geosciences, 50, 609–637.

    Article  Google Scholar 

  • Lee, S. J. (2005). Models of soft data in geostatistics and their application in environmental and health mapping. PhD thesis, University of North Carolina, Chapel Hill.

  • Lee, J., & Mukerji, T. (2012). The Stanford VI-E Reservoir: A Synthetic Data Set for Joint Seismic-EM Time-Lapse Monitoring Algorithms: 25th Annual Report: Technical Report. Stanford Center for Reservoir Forecasting.

  • Le L’och, G., & Galli, A. (1997). Truncated plurigaussian method: theoretical and practical points of view. In E. Y. Baafi & N. A. Schofield (Eds.), Geostatistics Wollongong’96 (pp. 211–222). Dordrecht: Springer.

    Google Scholar 

  • Li, X., Chen, Q., Wu, C., Liu, H., & Fang, Y. (2016). Application of multi-seismic attributes analysis in the study of distributary channels. Marine and Petroleum Geology, 75, 192–202.

    Article  Google Scholar 

  • Liu, Y., Harding, A., Abriel, W. L., & Strebelle, S. (2004). Multiple-point simulation integrating wells, three-dimensional seismic data, and geology. AAPG Bulletin, 88, 905–921.

    Article  Google Scholar 

  • Madani, N., & Abulkhair, S. (2020). A hierarchical cosimulation algorithm integrated with an acceptance–rejection method for the geostatistical modeling of variables with inequality constraints. Stochastic Environmental Research and Risk Assessment, 34, 1559–1589.

    Article  Google Scholar 

  • Madani, N., Biranvand, B., Naderi, A., & Keshavarz, N. (2019). Lithofacies uncertainty modeling in a siliciclastic reservoir setting by incorporating geological contacts and seismic information. Journal of Petroleum Exploration and Production Technology, 9, 1–16.

    Article  Google Scholar 

  • Madani, N., & Emery, X. (2017). Plurigaussian modeling of geological domains based on the truncation of non-stationary Gaussian random fields. Stochastic Environmental Research and Risk Assessment, 31, 893–913.

    Article  Google Scholar 

  • Maleki, M., Jélvez, E., Emery, X., & Morales, N. (2020). Stochastic open-pit mine production scheduling: A case study of an iron deposit. Minerals, 10(7), 585.

    Article  Google Scholar 

  • Maleki, M., Madani, N., & Jélvez, E. (2021). Geostatistical algorithm selection for mineral resources assessment and its impact on open-pit production planning considering metal grade boundary effect. Natural Resources Research, 30, 4079–4094.

    Article  Google Scholar 

  • Mallet, J. (2004). Space–time mathematical framework for sedimentary geology. Mathematical Geology, 36, 1–32.

    Article  Google Scholar 

  • Mariéthoz, G., Renard, P., Cornaton, F., & Jaquet, O. (2009). Truncated plurigaussian simulations to characterize aquifer heterogeneity. Groundwater, 47, 13–24.

    Article  Google Scholar 

  • Marzavan, S., & Sebacher, B. (2021). A new methodology based on finite element method (FEM) for generation of the probability field of rock types from subsurface. Arabian Journal of Geosciences, 14, 843.

    Article  Google Scholar 

  • Matheron, G., Beucher, H., de Fouquet, C., & Guérillot, D. (1987). Conditional simulation of the geometry of fluvio-deltaic reservoirs. SPE Annual Technical Conference and Exhibition, Dallas, Texas. https://doi.org/10.2118/16753-MS

    Article  Google Scholar 

  • Mehrabi, H., & Rahimpour-Bonab, H. (2014). Paleoclimate and tectonic controls on the depositional and diagenetic history of the Cenomanian–early Turonian carbonate reservoirs, Dezful Embayment. SW Iran. Facies, 60(1), 147–167.

    Article  Google Scholar 

  • Mery, N., Emery, X., Cáceres, A., Ribeiro, D., & Cunha, E. (2017). Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geology Reviews, 88, 336–351.

    Article  Google Scholar 

  • Monfaredi, K., Emami Niri, M., & Sedaee, B. (2020). Improving forecast uncertainty quantification by incorporating production history and using a modified ranking method of geostatistical realizations. Journal of Energy Resources Technology, 142(9), 093004.

    Article  Google Scholar 

  • Mohammadi, H., Hosseini, S. T., Asghari, O., da Silva, C. Z., & Boisvert, J. B. (2021). A direct sampling multiple point statistical approach for multivariate imputation of unequally sampled compositional variables and categorical data. Computers & Geosciences, 156, 104911.

    Article  Google Scholar 

  • Nazari Ostad, M., Emami Niri, M., & Darjani, M. (2018). 3D modeling of geomechanical elastic properties in a carbonate-sandstone reservoir: A comparative study of geostatistical co-simulation methods. Journal of Geophysics and Engineering, 15(4), 1419–1431.

    Article  Google Scholar 

  • Pyrcz, M. J., & Deutsch, C. V. (2014). Geostatistical reservoir modeling. Oxford University Press.

    Google Scholar 

  • Raeesi, M. A., Moradzadeh, A., Ardejani, F. D., & Rahimi, M. (2012). Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks. Journal of Petroleum Science and Engineering, 82, 151–165.

    Article  Google Scholar 

  • Rahimi, H., Asghari, O., & Afshar, A. (2018). A geostatistical investigation of 3D magnetic inversion results using multi-Gaussian kriging and sequential Gaussian co-simulation. Journal of Applied Geophysics, 154, 136–149.

    Article  Google Scholar 

  • Ravenne, C., Galli, A., Doligez, B., Beucher, H., & Eschard, R. (2002). Quantification of facies relationships via proportion curves. In M. Armstrong, C. Bettini, N. Champigny, A. Galli, & A. Remacre (Eds.), Geostatistics Rio 2000. Quantitative Geology and Geostatistics. (Vol. 12). Dordrecht: Springer. https://doi.org/10.1007/978-94-017-1701-4_3

    Chapter  Google Scholar 

  • Rezaee, H., & Marcotte, D. (2017). Integration of multiple soft data sets in MPS thru multinomial logistic regression: A case study of gas hydrates. Stoch Environmental Research Risk Assess, 31, 1727–1745.

    Article  Google Scholar 

  • Sabrian, P. G., Saepuloh, A., Kashiwaya, K., & Koike, K. (2021). Combined SBAS-InSAR and geostatistics to detect topographic change and fluid paths in geothermal areas. Journal of Volcanology and Geothermal Research, 416, 107272.

    Article  Google Scholar 

  • Sadeghi, M., Madani, N., Falahat, R., Sabeti, H., & Amini, N. (2022). Hierarchical reservoir lithofacies and acoustic impedance simulation: Application to an oil field in SW of Iran. Journal of Petroleum Science and Engineering, 208, 109552.

    Article  Google Scholar 

  • Sebacher, B., Hanea, R., & Marzavan, S. (2019). Conditioning the probability field of facies to facies observations using a regularized Element-free Galerkin (EFG) method, Petroleum Geostatistics 2019, Florence (Italy), pp. 1–5, https://doi.org/10.3997/2214-4609.201902249

  • Sebacher, B., Hanea, R., & Stordal, A. (2017). An adaptive pluri-gaussian simulation model for geological uncertainty quantification. Journal of Petroleum Science and Engineering, 158, 494–508.

    Article  Google Scholar 

  • Sbrana, A., Marianelli, P., Pasquini, G., Costantini, P., Palmieri, F., Ciani, V., & Sbrana, M. (2018). The integration of 3D modeling and simulation to determine the energy potential of low-temperature geothermal systems in the Pisa (Italy) sedimentary plain. Energies, 11(6), 1591.

    Article  Google Scholar 

  • Shragge, J., Bourget, J., Lumley, D. E., Giraud, J., Wilson, T. H., Iqbal, A., Emami Niri, M., Whitney, B. B., Potter, T., Miyoshi, T., & Witten, B. (2019). The Western Australia modeling project — Part 1: Geomodel building. Interpretation, 7(4), T773–T791.

    Article  Google Scholar 

  • Stephen, K. D., Clark, J. D., & Gardiner, A. R. (2001). Outcrop-based stochastic modelling of turbidite amalgamation and its effects on hydrocarbon recovery. Petroleum Geoscience, 7, 163–172.

    Article  Google Scholar 

  • Strebelle, S., & Cavelius, C. E. (2013). Solving speed and memory issues in multiple-point statistics simulation program SNESIM. Mathematical Geosciences, 46, 171–186.

    Article  Google Scholar 

  • Strebelle, S. (2020). Multiple-point statistics simulation models: Pretty pictures or decision-making tools. Mathematical Geosciences, 53, 267–278.

    Article  Google Scholar 

  • Talebi, H., Mueller, U. A., Tolosana-Delgado, R., & van den Boogaart, K. G. (2018). Geostatistical simulation of geochemical compositions in the presence of multiple geological units: Application to mineral resource evaluation. Mathematical Geosciences, 51, 129–153.

    Article  Google Scholar 

  • Talebi, H., Sabeti, E., Azadi, M., & Emery, X. (2016). Risk quantification with combined use of lithological and grade simulations: Application to a porphyry copper deposit. Ore Geology Reviews, 75, 42–51.

    Article  Google Scholar 

  • Thomas, H., Brigaud, B., Zeyen, H., Blaise, T., Andrieu, S., Catinat, M., Davaux, M., & Antics, M. (2020). Facies, porosity and permeability prediction and 3-D geological static model in the Middle Jurassic geothermal reservoir of the Paris Basin by integration of well logs and geostatistical modeling. 22nd EGU General Assembly, id.20712. https://doi.org/10.5194/egusphere-egu2020-20712

  • Wang, L., Yin, Y., Zhang, C., Feng, W., Li, G., Chen, Q., & Chen, M. (2022). A MPS-based novel method of reconstructing 3D reservoir models from 2D images using seismic constraints. Journal of Petroleum Science and Engineering, 209, 109974.

    Article  Google Scholar 

  • Zahmatkesh, I., Kadkhodaie, A., Soleimani, B., & Azarpour, M. (2021). Integration of well log-derived facies and 3D seismic attributes for seismic facies mapping: A case study from mansuri oil field, SW Iran. Journal of Petroleum Science and Engineering, 202, 108563.

    Article  Google Scholar 

  • Zhao, Y., Reynolds, A., & Li, G. (2008). Generating facies maps by assimilating production data and seismic data with the Ensemble Kalman Filter. SPE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, USA. https://doi.org/10.2118/113990-MS

    Article  Google Scholar 

  • Zhou, F., Shields, D. J., Tyson, S., & Esterle, J. S. (2018). Comparison of sequential indicator simulation, object modelling and multiple-point statistics in reproducing channel geometries and continuity in 2D with two different spaced conditional datasets. Journal of Petroleum Science and Engineering, 166, 718–730.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Prof. Emmanuel John Carranza and the reviewers for their comments, which helped us improve our paper.

Funding

This research has been partially supported by the National Agency for Research and Development of Chile (ANID), under grant ANID PIA AFB220002 (X.E.) (ANID PIA AFB180004).

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Authors and Affiliations

  1. Simulation and Data Processing Laboratory, School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammadali Rezaei, Omid Asghari & Sajjad Talesh Hosseini

  2. Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Emami Niri

  3. Department of Mining Engineering, University of Chile, Santiago, Chile

    Xavier Emery

  4. Advanced Mining Technology Center, University of Chile, Santiago, Chile

    Xavier Emery

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  1. Mohammadali Rezaei
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Correspondence to Omid Asghari.

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Rezaei, M., Emami Niri, M., Asghari, O. et al. Seismic Data Integration Workflow in Pluri-Gaussian Simulation: Application to a Heterogeneous Carbonate Reservoir in Southwestern Iran. Nat Resour Res 32, 1147–1175 (2023). https://doi.org/10.1007/s11053-023-10198-0

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