4D Seismic and Production History Matching, a Combined Formulation Using Hausdorff and FréChet Metric
- Qi Zhang (Heriot-Watt University) | Romain Chassagne (Heriot-Watt University) | Colin MacBeth (Heriot-Watt University)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 81st EAGE Conference and Exhibition, 3-6 June, London, England, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- History Matching, FrÃ©chet distance, Hausdorff distance, 4D Seismic Data, Binary Images
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- 88 since 2007
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Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.
|File Size||1 MB||Number of Pages||11|
Al-Shamma, B., Gosselin, O. & King, P.(2018) History Matching Using Hybrid Parameterisation and Optimisation Methods. [Online]. Available from : doi:10.2118/190776-MS.
Amini, H. & Alvarez, E.(2014) Calibration of the Petro-elastic Model (PEM) for 4D Seismic Studies in Multimineral Rocks. [Online]. Available from: doi:10.3997/2214-4609.20132136.
Aranha, C., Tanabe, R., Chassagne, R. & Fukunaga, A.(2015) Optimization of oil reservoir models using tuned evolutionary algorithms and adaptive differential evolution. 2015 IEEE Congress on Evolutionary Computation (CEC). [Online]. pp.877–884. Available from: doi:10.1109/CEC.2015.7256983.
Bagley, G., Saxby, I., McGarrity, J., Pearse, C., (2004) 4D/Time-Lapse Seismic: Examples from the Foinaven, Schiehallion and Loyal Fields, UKCS, West of Shetland. [Online]. Available from: doi:10.1144/GSL.MEM.2004.029.01.27.
Bouzarkouna, Z. & Nobakht, B.N.K. (2015) A Better Formulation of Objective Functions for History Matching Using Hausdorff Distances. EUROPEC 2015. [Online]. p.9. Available from: doi:10.2118/174302-MS.
Campbell, S., Schons, M., Mathew, S., Riley, D., (2015) Optimising Reservoir Management With Improved Time-Lapse Processing on 10 Acquisition Vintages Over the Foinaven-Schiehallion-Loyal Fields. International Petroleum Technology Conference. [Online]. Available from: doi:10.2523/IPTC-18453-MS.
Charitopoulos, V.M. & Dua, V.(2017) A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty. Applied Energy. [Online] 186539-548. Available from: doi:https://doi.org/10.1016/j.apenergy.2016.05.082.
Chen, Y., Chang, H., Meng, J. & Zhang, D.(2019) Ensemble Neural Networks (ENN): A gradient-free stochastic method. Neural Networks. [Online] 110170-185. Available from: doi:https://doi.org/10.1016/j.neunet.2018.11.009.
Deb, K. (1999) Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation. [Online] 7 (3), 205–230. Available from: doi:10.1162/evco.1918.104.22.168.
De Souza, R., Lumley, D., Shragge, J., Davolio, A., (2018) Analysis of Time-Lapse Seismic and Production Data for Reservoir Model Classification and Assessment. [Online]. Available from: doi:10.1088/1742-2140/aab287.
Falahat, R., Obidegwu, D., Shams, A. & MacBeth, C.(2014) The interpretation of amplitude changes in 4D seismic data arising from gas exsolution and dissolution. Petroleum Geoscience. [Online] 20 (3), 303–320. Available from: doi:10.1144/petgeo2014-008.
Geng, C., MacBeth, C. & Chassagne, R.(2017) Seismic History Matching Using a Fast-Track Simulator to Seismic Proxy. SPE Europec featured at 79th EAGE Conference and Exhibition. [Online]. p.13. Available from: doi:10.2118/185822-MS.
Huttenlocher, D.P., Rucklidge, W.J. & Klanderman, G. a. (1992) Comparing images using the Hausdorff distance under translation. Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [Online]. 15 (9) pp.850–863. Available from: doi:10.1109/CVPR.1992.223209.
Kretz, V., Le Ravalec-Dupin M. & Roggero, F.(2004) An Integrated Reservoir Characterization Study Matching Production Data and 4D Seismic. SPE Reservoir Evaluation & Engineering. [Online] Available from: doi:10.2118/88033-PA.
Leach, H.M., Herbert, N., Los, A. & Smith, R.L. (1999) The Schiehallion development. Geological Society, London, Petroleum Geology Conference series. [Online] 5 (1), 683–692. Available from: doi:10.1144/0050683.
Mohamed, L., Christie, M.A. & Demyanov, V.(2010) Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. SPE Journal. [Online] 15 (01), 31–38. Available from: doi:10.2118/119139-PA.
Taha, A.A. & Hanbury, A.(2015) An Efficient Algorithm for Calculating the Exact Hausdorff Distance. IEEE Trans. Pattern Anal. Mach. Intell. [Online] 37 (11), 2153–2163. Available from: doi:10.1109/TPAMI.2015.2408351.
Tanabe, R. & Fukunaga, A.S. (2014) Improving the search performance of SHADE using linear population size reduction. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. [Online] 1658-1665. Available from: doi:10.1109/CEC.2014.6900380.
Trice, M.L.Jr. & Dawe, B.A. (1992) Reservoir Management Practices. Journal of Petroleum Technology. [Online] 44 (12), 1344–1349. Available from: doi:10.2118/22236-PA.
Zhang, Q., Chassagne, R. & MacBeth, C.(2018) Seismic history matching uncertainty with weighted objective functions. In: 16th European Conference on the Mathematics of Oil Recovery. [Online]. 2018 Netherlands, EAGE Publishing BV. Available from: doi:10.3997/2214-4609.201802282.