Scientific and technical journal

«Equipment and technologies for oil and gas complex»

ISSN 1999-6934


UDC: 622.276:532:519.876
DOI: 10.33285/1999-6934-2021-1(121)-55-61



1 Tomsk Polytechnic University, Tomsk, Russian Federation

Keywords: mismatch expression; mismatch; component composition; objective function; automated history matching; automated history matching of a model; optimization algorithms; history matching quality; reservoir geological-hydrodynamic simulation; oil field; numerical model


The paper is devoted to the two aspects of the objective function formulation for automated history matching of geological-hydrodynamic hydrocarbon reservoir models, which are the mismatch expression and the component composition of the objective function. Numerical modeling is used in the development of oil and gas fields to improve the efficiency of hydrocarbon reserves recovery. Before using the model for forecasting it is matched to the production history data, so that the model will reproduce the historically observed parameters of the wells operation. The optimization algorithm and the objective function are the integral parts of the automated history matching. The objective function expresses the deviation of the calculated well performance indicators from the actual ones and allows the optimizer to find the inverse problems solutions. The purpose of this work is to study the influence of the mismatch expression and the component composition of the objective function on history matching efficiency. The novelty of the research lies in the study of the influence of three types of mismatch expressions of the objective function on history matching, both analytically and confirming by multiple computational experiments. The results of this study helped to develop recommendations regarding the mismatch expression and the component composition of the objective function for reservoir model history auto-matching.


1. Геологически обоснованная автоматизированная адаптация гидродинамических моделей на примере реального месторождения / Г.Ю. Шишаев, И.В. Матвеев, Г.А. Еремян [и др.] // Нефт. хоз-во. – 2020. – № 6. – С. 58–61. – DOI: 10.24887/0028-2448-2020-6-58-61
2. Begashaw G.B., Yohannes Y.B. Review of Outlier Detection and Identifying Using Robust Regression Model // Int. J. of Systems Science and Applied Mathematics. – 2020. – Vol. 5, Issue 1. – P. 4–11. – DOI: 10.11648/j.ijssam.20200501.12
3. Bertolini A.C., Schiozer J.D. Influence of the objective function in the history matching process // J. of Petroleum Science and Engineering. – 2011. – Vol. 78, Issue 1. – P. 32–41. – DOI: 10.1016/j.petrol.2011.04.012
4. Bouzarkouna Z., Nobakht B. A Better Formulation of Objective Functions for History Matching Using Hausdorff Distances // Conf. EUROPEC 2015, Madrid, Spain. – SPE-174302-MS. – 2015.
5. Use of Multi-Objective Algorithms in History Matching of a Real Field / M. Christie, D. Eydinov, V. Demyanov [et al.] // Conf. SPE Reservoir Simulation Symposium. – SPE-163580-MS. – 2013.
6. How does the definition of the objective function influence the outcome of history matching? / G. Eremyan, I. Matveev, G. Shishaev [et al.] // Conf. Proc. ECMOR XVII. – 2020. – Vol. 2020. – P. 1–14.
7. Hutahaean J.J., Demyanov V., Christie M.A. On Optimal Selection of Objective Grouping for Multiobjective History Matching // SPE J. – SPE-185957-PA. – 2017. – P. 1296–1312. – DOI: 10.2118/185957-PA
8. Hutahaean J.J., Demyanow V., Christie M.A. Impact of Model Parameterisation and Objective Choices on Assisted History Matching and Reservoir Forecasting // SPE/IATMI Asia Pacific Oil & Gas Conf. and Exhibition, Nusa Dua, Bali, Indonesia. – SPE-176389-MS. – 2015.
9. Lehmann E.L., Casella G. Theory of Point Estimation. – 2nd ed. – New York: Springer, 1998. – 590 p.
10. Geology realism control in automated history matching / I. Matveev, G. Shishaev, G. Eremyan [et al.] // Conf. Proc. ECMOR XVII. – European Association of Geoscientists & Engineers, 2020. – Vol. 2020. – P. 1–9.
11. Multiobjective Optimization With Application to Model Validation and Uncertainty / R.W. Schulze-Riegert, M. Krosche, A. Fahimuddin, S.G. Ghedan // SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain. – SPE-105313-MS. – 2007.
12. Vink J.C., Goa G., Chen C. Bayesian Style History Matching: Another Way to Under-Estimate Forecast Uncertainty? // SPE Annual Technical Conference and Exhibition, Houston, TX. – SPE-175121-MS. – 2015.