- Al-Anazi A., Babadagli T. (2009) Automatic fracture density update using smart data and artificial neural networks, Comput. Geosci. 36, 3, 335–347, https://doi.org/10.1016/j.cageo.2009.08.005. [CrossRef] [Google Scholar]
- Allan J., Sun S.Q. (2003, January) Controls on recovery factor in fractured reservoirs: lessons learned from 100 fractured fields, SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/84590-MS. [Google Scholar]
- Almeida Netto S.L., Schiozer D.J., Ligero E.L., Maschio C. (2003, January) History matching using uncertainty analysis, Canadian International Petroleum Conference, Petroleum Society of Canada, https://doi.org/10.2118/2003-145. [Google Scholar]
- Al-Harbi M., Cheng H., He Z., Datta-Gupta A. (2004, September) Streamline-Based Production Data Integration in Naturally Fractured Reservoirs, SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/89914-PA. [Google Scholar]
- Avansi G.D., Maschio C., Schiozer D.J. (2016) Simultaneous history matching approach using reservoir-characterization and reservoir-simulation studies, SPE Reserv. Evalu. Eng. 19, 694–712, https://doi.org/10.2118/179740-PA. [Google Scholar]
- Bahar A., Ates H., Al-Deeb M.H., Salem S.E., Badaam H., Kelkar M. (2003, January) Practical approach in modeling naturally fractured reservoir: A filed case study SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/84078-MS. [Google Scholar]
- Basbug B., Karpyn Z.T. (2008, October) Determination of relative permeability and capillary pressure curves using automated history-matching approach, SPE Eastern Regional/AAPG Eastern Section Joint Meeting, Society of Petroleum Engineers, https://doi.org/10.2118/117767-MS. [Google Scholar]
- Bourbiaux B. (2010) Fractured reservoir simulation: A challenging and rewarding issue, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 65, 2, 227–238, https://doi.org/10.2516/ogst/2009063. [CrossRef] [Google Scholar]
- Caers J. (2002, September) Geostatistical History matching under training-image based geological model constraints, SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/77429-MS. [Google Scholar]
- Caers J. (2003) Efficient gradual deformation using streamline-based proxy method, J. Petrol. Sci. Eng. 39, 57–83, https://doi.org/10.1016/S0920-4105(03)00040-8. [CrossRef] [Google Scholar]
- Correia M.G., Maschio C., von Hohendorff Filho J.C., Schiozer D.J. (2016) The impact of time-dependent matrix-fracture fluid transfer in upscaling match procedures, J. Petrol. Sci. Eng. 146, 752–763, https://doi.org/10.1016/j.petrol.2016.07.039. [CrossRef] [Google Scholar]
- Cui H., Kelkar M. (2005, April) Automatic history matching of naturally fractured reservoir and a case study, SPE Western Regional Meeting, Society of Petroleum Engineers, https://doi.org/10.2118/94037-MS. [Google Scholar]
- De Lima A., Lange A., Schiozer D.J. (2009, June) Assisted history matching for the characterization and recovery optimization of fractured reservoirs using connectivity analysis, EAGE Annual Conference & Exhibition incorporating SPE Europec, Society of Petroleum Engineers, https://doi.org/10.2118/154392-MS. [Google Scholar]
- Feraille M., Marrel A. (2012) Prediction under uncertainty on a mature field, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 67, 2, 193–206. [CrossRef] [Google Scholar]
- Firoozabadi A. (2000) Recovery mechanisms in fractured reservoirs and field performance, J. Can. Pet. Technol. 39, 11, 13–17, https://doi.org/10.2118/00-11-DAS. [CrossRef] [Google Scholar]
- Ford A., Flynn H. (2005) Statistical screening of system dynamic models, Syst. Dyn. Rev. 21, 4, 273–303, https://doi.org/10.1002/sdr.322. [CrossRef] [Google Scholar]
- Gang T., Kelkar M. (2006, April) Efficient history matching in naturally fractured reservoirs SPE/DOE Symposium on Improved Oil Recovery, Society of Petroleum Engineers, https://doi.org/10.2118/99578-MS. [Google Scholar]
- Gang T., Kelkar M. (2008) History matching for determination of fracture permeability and capillary pressure, SPE Reserv. Evalu. Eng. 11, 5, 813–822, https://doi.org/10.2118/101052-PA. [Google Scholar]
- Ghaedi M., Masihi M., Heinemann Z.E., Ghazanfari M.H. (2015) History matching of naturally fractured reservoirs based on the recovery curve method, J. Petrol. Sci. Eng. 126, 211–221, https://doi.org/10.1016/j.petrol.2014.12.002. [CrossRef] [Google Scholar]
- Gilman J.R. (2003, June) Practical aspects of simulation of fractured reservoirs, International forum on reservoir simulation, pp. 23–27, Buhl, Baden-Baden, Germany. [Google Scholar]
- Gilman J.R., Kazemi H. (1983) Improvements in simulation of naturally fractured reservoirs, Soc. Pet. Eng. J. 23, 4, 695–707. [CrossRef] [Google Scholar]
- Ginting V., Pereira F., Presho M., Wo S. (2011) Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model, Comput. Geosci. 15, 4, 691–707, https://doi.org/10.1007/s10596-011-9236-4. [CrossRef] [Google Scholar]
- Hu L.Y. (2003, April) History matching of object-based stochastic reservoir models, 13th Middle East Oil Show & Conference, Society of Petroleum Engineers, https://doi.org/10.2118/81503-MS [Google Scholar]
- Hu L.Y., Blanc G. (1998, September) Constraining a reservoir facies model to dynamic data using a gradual deformation method, ECMOR VI-6th European Conference on the Mathematics of Oil Recovery, European Association of Geoscientists & Engineers, https://doi.org/10.3997/2214-4609.201406609 [Google Scholar]
- Hu L.Y., Jenni S. (2005) History Matching of Object-Based Stochastic Reservoir Models, SPE J. 10, 3, 312–323, https://doi.org/10.2118/81503-MS. [CrossRef] [Google Scholar]
- Jenni S., Hu L.Y., Basquet R., de Marsily G., Bourbiaux B. (2007) History matching of a stochastic model of field-scale fractures: methodology and case study, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 62, 2, 265–276, https://doi.org/10.2516/ogst:2007022. [CrossRef] [Google Scholar]
- Jung A., Fenwick D., Caers J. (2013) Updating uncertainty in the conceptual geological representation of fractured reservoirs using production data, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC, Society of Petroleum Engineers, https://doi.org/10.3997/2214-4609.20130924 [Google Scholar]
- Kassenov B., King G.R., Chaudhri M., Abdrakhmanova A., Jenkins S., Bateman P., Iskakov E. (2014, November) Efficient workflow for assisted history matching and brownfield design of experiments for the Tengiz field, SPE Annual Caspian Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/172329-MS. [Google Scholar]
- Kuchuk F., Biryukov D., Fitzpatrick T. (2015) Fractured-reservoir modeling and interpretation, SPE J. 20, 5, 983–1004,https://doi.org/10.2118/176030-PA. [CrossRef] [Google Scholar]
- Lange A.G. (2009) Assisted history matching for the characterization of fractured reservoirs, AAPG Bull. 93, 11, 1609–1619, https://doi.org/10.1306/08040909050. [CrossRef] [Google Scholar]
- Long J.C.S., Remer J.S., Wilson C.R., Witherspoon P.A. (1982) Porous media equivalents for networks of discontinuous fractures, Water Resour. Res. 18, 3, 645–658, https://doi.org/10.1029/WR018i003p00645. [CrossRef] [Google Scholar]
- Lu L., Zhang D. (2015) Assisted history matching for fractured reservoirs by use of hough-transform-based parameterization, SPE J. 20, 5, 942–961, https://doi.org/10.2118/176024-PA. [CrossRef] [Google Scholar]
- Maschio C., Schiozer D.J. (2016) Probabilistic history matching using discrete Latin Hypercube sampling and nonparametric density estimation, J. Petrol. Sci. Eng. 147, 98–115, https://doi.org/10.1016/j.petrol.2016.05.011. [CrossRef] [Google Scholar]
- Mazo E.O.M., Schiozer D.J. (2013, June) Modeling fracture relative permeability-what is the best option? 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013, Society of Petroleum Engineers, https://doi.org/10.3997/2214-4609.20130867 [Google Scholar]
- Mckay M.D., Morrison J.D., Upton S.C. (1999) Evaluating prediction uncertainty in simulation models, Comput. Phys. Commun. 117, 44–51, https://doi.org/10.1016/S0010-4655(98)00155-6. [CrossRef] [Google Scholar]
- Müller C., Siegesmund S., Blum P. (2010) Evaluation of the representative elementary volume (REV) of a fractured geothermal sandstone reservoir, Environ. Earth Sci. 61, 8, 1713–1724. https://doi.org/10.1007/s12665-010-0485-7. [CrossRef] [Google Scholar]
- Nejadi S., Leung J.Y., Trivedi J.J., Virues C. (2015) Integrated characterization of hydraulically fractured shale-gas reservoirs – production history matching, SPE Reserv. Evalu. Eng. 18, 4, 481–494, https://doi.org/10.2118/171664-PA. [CrossRef] [Google Scholar]
- Nelson R.A. (2001) Geologic analysis of naturally fractured reservoirs, Gulf Professional Publishing, Houston, TX. [Google Scholar]
- Oliver D.S., Chen Y. (2011) Recent progress on reservoir history matching: a review, Comput. Geosci. 15, 1, 185–221, https://doi.org/10.1007/s10596-010-9194-2. [CrossRef] [Google Scholar]
- Paico D.H.R. (2008) A new procedure for history matching naturally fractured reservoirs (Dissertation, Stanford University, USA). Retrieved from https://pangea.stanford.edu/ERE/pdf/pereports/Engineer/Rojas08.pdf? [Google Scholar]
- Rotondi M., Nicotra G., Godi A., Contento F.M., Blunt M.J., Christie M. (2006, January) Hydrocarbon production forecast and uncertainty quantification: A field application SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, https://doi.org/10.2118/102135-MS. [Google Scholar]
- Royer P., Auriault J.L., Lewandowska J., Serres C. (2002) Continuum modelling of contaminant transport in fractured porous media, Transp. Porous Media 49, 3, 333–359, https://doi.org/10.1023/A:1016272700063. [CrossRef] [Google Scholar]
- Rwechungura R.W., Dadashpour M., Kleppe J. (2011, March) Advanced history matching techniques reviewed, SPE Middle East Oil and Gas Show and Conference, Society of Petroleum Engineers, https://doi.org/10.2118/142497-MS. [Google Scholar]
- Schiozer D.J., Avansi G.D., dos Santos A.A.S. (2015) Risk Quantification combining geostatistical realizations and discretized Latin Hypercube, J. Braz. Soc. Mech. Sci. Eng. 1–13, https://doi.org/10.1007/s40430-016-0576-9. [Google Scholar]
- Suzuki S., Daly C., Caers J., Mueller D. (2007) History matching of naturally fractured reservoirs using elastic stress simulation and probability perturbation method, SPE J. 12, 1, 118–129, https://doi.org/10.2118/95498-PA. [CrossRef] [Google Scholar]
- Taylor T.R.B., Ford D.N., Ford A. (2010, December) Improving model understanding using statistical screening, Proceedings of the 2010 Winter Simulation Conference (WSC), https://doi.org/10.1002/sdr.428. [Google Scholar]
- Tolstukhin E., Lyngnes B., Sudan H.H. (2012, June) Ekofisk 4D seismic history matching workflow, EAGE Annual Conference & Exhibition incorporating EUROPEC, Society of Petroleum Engineers, https://doi.org/10.2118/154347-MS [Google Scholar]
- Tran N.H..(2004). Characterization and modelling of naturally fracture reservoirs (Doctoral thesis, University of New South Wales, Sydney, Australia). Retrieved from http://handle.unsw.edu.au/1959.4/20559. [Google Scholar]
- Vasco D.W., Datta-Gupta A., Long J.C.S. (1997) Integrating field production history in stochastic reservoir characterization, SPE Form. Eval. 12, 3, 149–156, https://doi.org/10.2118/36567-PA. [CrossRef] [Google Scholar]
- Verscheure M., Fourno A., Chilès J.P. (2012) Joint inversion of fracture model properties for CO2 storage monitoring or oil recovery history matching, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 67, 2, 221–235, https://doi.org/10.2516/ogst/2011176. [CrossRef] [Google Scholar]
- Warren J.E., Root P.J. (1963) The behavior of naturally fractured reservoirs, Soc. Pet. Eng. J. 3, 3, 245–255. [CrossRef] [Google Scholar]
Open Access
Numéro |
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
|
|
---|---|---|
Numéro d'article | 41 | |
Nombre de pages | 23 | |
DOI | https://doi.org/10.2516/ogst/2018038 | |
Publié en ligne | 2 octobre 2018 |
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.