Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies
- Guilherme Daniel Avansi (State Univeristy of Campinas) | Célio Maschio (Unicamp) | Denis Jose Schiozer (UNICAMP)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- October 2016
- Document Type
- Journal Paper
- 694 - 712
- 2016.Society of Petroleum Engineers
- Petroleum, Reservoir Characterization, Reservoir Simulation, Reservoir Engineering, History Matching
- 9 in the last 30 days
- 430 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D-seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated-history- matching studies use a unique objective function (OF), this is not enough. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization, applying a simultaneous calibration of different OFs in a history-matching procedure, and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.
|File Size||2 MB||Number of Pages||19|
Agbalaka, C. and Oliver, D. 2008. Application of the EnKF and Localization to Automatic History Matching of Facies Distribution and Production Data. Math. Geosci. 40 (4): 353–374. http://dx.doi.org/10.1007/s11004-008-9155-7.
ANP. 2000. National Agency of Petroleum, Natural Gas and Biofuels of Brazil. Production and Exploration Data Sets. (BDEP, Banco de Dados de Explora?ão e Produ?ão). http://www.bdep.gov.br/.
Avansi, G. D. and Schiozer, D. J. 2015. UNISIM-I: Synthetic Model for Reservoir Development and Management Applications. Int. J. Model. Simul. Pet. Ind. 9 (1): 21–30.
Behrens, R. A. and Tran, T. T. 1998. Incorporating Seismic Data of Intermediate Vertical Resolution Into 3D Reservoir Models. Presented at the SPE Technical Conference and Exhibition. Society of Petroleum Engineers, New Orleans, 27–30 September. SPE-49143-MS. http://dx.doi.org/10.2118/49143-MS.
Bissell, R. C., Dubrule, O., Lamy, P. et al. 1997. Combining Geostatistical Modelling With Gradient Information for History Matching: The Pilot Point Method. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5–8 October. SPE-38730-MS. http://dx.doi.org/10.2118/38730-MS.
Costa, L. A. N., Maschio, C. and José Schiozer, D. 2014. Application of Artificial Neural Networks in a History Matching Process. J. Pet. Sci. Eng. 123 (November): 30–45. http://dx.doi.org/10.1016/j.petrol.2014.06.004.
Floris, F. 1996. Direct Conditioning of Gaussian Random Fields to Dynamic Production Data. Presented at ECMOR V – 5th European Conference on the Mathematics of Oil Recovery, Leoben, Austria, 3–6 September.
Gosselin, O., Aanonsen, S. I., Aavatsmark, I. et al. 2003. History Matching Using Time-Lapse Seismic (HUTS). Presented at the SPE Annual Technical Conference and Exhibition, Denver, 5–8 October. SPE-84464-MS. http://dx.doi.org/10.2118/84464-MS.
Hoffman, B. T. and Caers, J. 2007. History Matching by Jointly Perturbing Local Facies Proportions and Their Spatial Distribution: Application to a North Sea Reservoir. J. Pet. Sci. Eng. 57 (3–4): 257–272. http://dx.doi.org/10.1016/j.petrol.2006.10.011.
Kazemi, A. and Stephen, K. D. 2012. Schemes for Automatic History Matching of Reservoir Modeling: A Case of Nelson Oilfield in UK. Petrol. Explor. Dev. 39 (3): 349–361. http://dx.doi.org/10.1016/S1876-3804(12)60051-2.
LaVenue, A. M. and Pickens, J. F. 1992. Application of a Coupled Adjoint Sensitivity and Kriging Approach to Calibrate a Groundwater Flow Model. Water Resour. Res. 28 (6): 1543–1569. http://dx.doi.org/10.1029/92wr00208.
LaVenue, A. M., RamaRao, B. S., De Marsily, G. et al. 1995. Pilot Point Methodology for Automated Calibration of an Ensemble of Conditionally Simulated Transmissivity Fields: 2. Application. Water Resour. Res. 31 (3): 495–516. http://dx.doi.org/10.1029/94wr02259.
Marsily, D., Lavedau, G., Boucher, M. et al. 1984. Interpretation of Interference Test in a Well Field Using Geostatistical Techniques to Fit the Permeability Distribution in a Reservoir Model. In Geostatistics for Natural Resources Characterization, ed. G. Verly, M. David, A. G. Journel, A. Marechal, 831–849. Dordreht, Holland: Reidel Publishing Company.
Maschio, C. and Schiozer, D. J. 2005. Development and Application of Methodology for Assisted History Matching. Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro, 20–23 June. SPE-94882-MS. http://dx.doi.org/10.2118/94882-MS.
Mattax, C. C. and Dalton, R. L. 1990. Reservoir Simulation. Richardson, Texas: Monograph Series, Society of Petroleum Engineers.
Mezghani, M., Fornel, A., Langlais, V. et al. 2004. History Matching and Quantitative Use of 4D Seismic Data for an Improved Reservoir Characterization. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 26–29 September. SPE-90420-MS. http://dx.doi.org/10.2118/90420-MS.
RamaRao, B. S., LaVenue, A. M., De Marsily, G. et al. 1995. Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments. Water Resour. Res. 31 (3): 475–493. http://dx.doi.org/10.1029/94wr02258.
Romero, C. E., Carter, J. N., Gringarten, A. C. et al. 2000. A Modified Genetic Algorithm for Reservoir Characterisation. Presented at the International Oil and Gas Conference and Exhibition in China, Beijing, 7–10 November. SPE-64765-MS. http://dx.doi.org/10.2118/64765-MS.
Sayyafzadeh, M., Haghighi, M. and Carter, J. N. 2012. Regularization in History Matching Using Multi-Objective Genetic Algorithm and Bayesian Framework. Presented with SPE Europec/EAGE Annual Conference, Copenhagen, Denmark, 4–7 June. SPE-154544-MS. http://dx.doi.org/10.2118/154544-MS.
Schiozer, D. J., Avansi, G. D. and Santos, A. A. 2016. Risk Quantification Combining Geoestatistical Realizations and Discretized Latin Hypercube. J. Braz. Soc. Mech. Sci. Eng (in press). http://dx.doi.org/10.1007/s40430-016-0576-9.
Skorstad, A., Kolbjørnsen, O., Drottning, Å. et al. 2006. Combining Saturation Changes and 4D Seismic for Updating Reservoir Characterizations. SPE Res Eval & Eng 9 (5): 502–512. SPE-106366-PA. http://dx.doi.org/10.2118/106366-PA.
Suzuki, S. and Caers, J. K. 2006. History Matching With an Uncertain Geological Scenario. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-102154-MS. http://dx.doi.org/10.2118/102154-MS.
Thomas, L. K., Hellums, L. J. and Reheis, G. M. 1972. A Nonlinear Automatic History Matching Technique for Reservoir Simulation Models. SPE J. 12 (6): 508–514. http://dx.doi.org/10.2118/3475-PA.
Voelker, J. 2004. A Reservoir Characterization of Arab-D Super-K as a Discrete Fracture Network Flow System, Ghawar Field, Saudi Arabia. PhD dissertation, Stanford University, Stanford, California (December 2004).
Watson, A. T. and Lee, W. J. 1986. A New Algorithm for Automatic History Matching Production Data. Presented at the SPE Unconventional Gas Technology Symposium, Louisville, Kentucky, 18–21 May. SPE-15228-MS. http://dx.doi.org/10.2118/15228-MS.
Xue, G. and Datta-Gupta, A. 1997. Structure Preserving Inversion: An Efficient Approach to Conditioning Stochastic Reservoir Models to Dynamic Data. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5–8 October. SPE-38727-MS. http://dx.doi.org/10.2118/38727-MS.