Page 297 - Intelligent Digital Oil And Gas Fields

Integrated Asset Management and Optimization Workflows 245

Maucec, M., Cullick, A.S., 2015. Systems and Methods for the Quantitative Estimate of
Production-Forecast Uncertainty. US Patent 9,223,042 B2.
Maucec, M., Douma, S., Hohl, D., Leguijt, J., Jimenez, E.A., Datta-Gupta, A., 2007.
Streamline-Based History Matching and Uncertainty: Markov-chain Monte Carlo
Study of an Offshore Turbidite Oil Field. SPE-109943-MS, https://doi.org/10.2118/
109943-MS.
Maucec, M., Cullick, S., Shi, G., 2011. Geology-guided Quantification of Production-
Forecast Uncertainty in Dynamic Model Inversion. SPE-146748-MS, https://doi.
org/10.2118/146748-MS.
Maucec, M., Singh, A., Carvajal, G., Mirzadeh, S., Knabe, S., Chambers, R., et al., 2013a.
Engineering Workflow for Probabilistic Assisted History Matching and Production
Forecasting: Application to a Middle East Carbonate Reservoir. SPE-165980-MS,
https://doi.org/10.2118/165980-MS.
Maucec, M., Singh, A., Carvajal, G., Mirzadeh, S., Knabe, S., Mahajan, A., et al., 2013b.
Next Generation of Workflows for Multi-level Assisted History Matching and Produc-
tion Forecasting: Concept, Collaboration and Visualization. SPE-167340-MS, https://
doi.org/10.2118/167340-MS.
Maucec, M., De Matos Ravanelli, F.M., Lyngra, S., Zhang, S.J., Alramadhan, A.A.,
Abdelhamid, O.A., Al-Garni, S.A., 2016. Ensemble-based Assisted History Matching
with Rigorous Uncertainty Quantification Applied to Naturally Fractured Carbonate
Reservoir. SPE-181325-MS, https://doi.org/10.2118/181325-MS.
Maucec, M., Awan, A., Benedek, L., Lyngra, S., 2017. Implementation of Assisted History
Matching Under Uncertainty in Integrated Reservoir Modeling. SPE-188049-MS,
https://doi.org/10.2118/188049-MS.
McVay, D.A., Dossary, M.N., 2014. The Value of Assessing Uncertainty. SPE-160189-PA,
https://doi.org/10.2118/160189-PA.
Mohaghegh, S.D., Abdulla, F., Abdou, M., Gaskari, R., Maysami, M., 2015. Smart Proxy:
An Innovative Reservoir Management Tool; Case Study of a Giant Mature Oilfield in
UAE. SPE-177829-MS, https://doi.org/10.2118/177829-MS.
Mohamed, L., Christie, M., Demyanov, V., 2010a. Comparison of Stochastic Sampling
Algorithms for Uncertainty Quantification. SPE-119139-PA, https://doi.org/
10.2118/119139-PA.
Mohamed, L., Christie, M., Demyanov, V., 2010b. Reservoir Model History Matching with
Particle Swarms: Variants Study. SPE-129152-MS, https://doi.org/10.2118/129152-MS.
Nævdal, G., Johnsen, L.M., Aanonsen, S.I., Vefring, E., 2003. Reservoir monitoring and
continuous model updating using the ensemble Kalman filter. In: SPE-84372-MS.
https://doi.org/10.2118/84372-PA.
Neal, R.M., 1993. Probabilistic Inference using Markov Chain Monte Carlo Methods.
Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto.
Nemirovski, A., Juditsky, A., Lan, G., Shapiro, A., 2009. Robust Stochastic Approximation
Approach to Stochastic Programming. SIAM J. Optim. 19 (4), 1574–1609 (Society for
Applied and Industrial Mathematics), .
Newman, A.J., 1996. Model Reduction via Karhunen-Loeve Expansion Part I: An exposition.
Institute for Systems Research and Electrical Engineering Department, University of
Maryland. http://citeseerx.ist.psu.edu/viewdoc/summary?doi¼10.1.1.50.6670&rank¼1.
Oberwinkler, C., Stundner, M., 2005. From Real-Time Data to Production Optimization.
SPE 87008-PA, https://doi.org/10.2118/87008-PA.
Olalotiti-Lawal, F., Datta-Gupta, A., 2015. A Multi-Objective Markov Chain Monte Carlo
Approach for History Matching and Uncertainty Quantification. SPE-175144-MS,
https://doi.org/10.2118/175144-MS.
Oliver, D.S., Chen, Y., 2011. Recent progress on reservoir history matching: a review.
Comput. Geosci. 15 (1), 185–221. http://www.academia.edu/22805522/Recent_
progress_on_reservoir_history_matching_a_review.