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Geologically plausible computer-aided history matching on the example of one the oil fields

UDK: 622.276.1/.4.001
DOI: 10.24887/0028-2448-2020-6-58-61
Key words: geological modeling, hydrodynamic modeling, history matching, geologically plausible model, objective function
Authors: G.Yu. Shishaev (Tomsk Polytechnic University, RF, Tomsk), I.V. Matveev (Tomsk Polytechnic University, RF, Tomsk), G.A. Eremyan (Tomsk Polytechnic University, RF, Tomsk), V.V. Demyanov (Heriot-Watt University, United Kingdom, Edinburgh), S.V. Kaygorodov (Gazpromneft NTC LLC, RF, Saint-Petersburg)

The existing methods for history matching (HM) in hydrodynamic modeling do not meet requirements for geologically plausible model. That significantly affects the quality and predictive ability of this models and hence the success of investment decisions. In this paper, the method of automatic HM is presented. The method provides geological parameters control with preserving of identified petrophysical and geological uncertainty that significantly affect reservoir development process. Firstly, based on measured data and data from fields-analogues, realistic limits for each parameter and relationship in model were identified. Further, in order to provide the geologically plausible simulation model within a given geological concept, variation of one parameter or relationship during HM leads to variation of others parameters related to it within previously identified uncertainty range. For example, changes in parameters for porosity calculations leads to changes in parameters for permeability calculations, other model characteristics which related to permeability and so on. Iterative algorithm «evolution strategy» is used for automatic HM. During HM objective function based on mismatch of calculated and historical data for 50 well of sector model of one of the oil fields is minimized. As a result, number adapted models were obtained that demonstrate a good quality of HM. Based on these adapted simulation models forecasting of wells’ working parameters is made with taking into account the uncertainty of the initial data and geological characteristics. The distinctive feature of proposed method is the rejection of single deterministic relationships between petrophysical and geological parameters in favor of variations of these parameters within identified uncertainty range. This method allows speeding up HM and providing control of geological realism of simulation models. As a result, confidence in forecasting based on a set of adapted and geologically plausible models is increased.

References

1. Hajizadeh Y., Christie M., Demyanov V., Comparative study of novel population-based optimization algorithms for history matching and uncertainty quantification: PUNQ-S3 revisited, SPE-136861-MS, 2010, https://doi.org/10.2118/136861-MS.

2. Rwechungura R., Dadashpour M., Advanced history matching techniques reviewed, SPE-142497-MS, 2011, https://doi.org/10.2118/142497-MS.

3. Cancelliere M., Verga F., Viberti D., Benefits and limitations of assisted history matching, SPE-146278-MS, 2011, https://doi.org/10.2118/146278-MS.

4.В  D.J. Schiozer, Almeida Netto S.L., Ligero E.L., Maschio C., Integration of history matching and uncertainty analysis, Journal of Canadian Petroleum Technology, 2005, V. 44, no. 7, https://doi.org/10.2118/05-07-02.

5. Yeh Tzu-Hao et al., Reservoir uncertainty quantification using probabilistic history matching workflow, SPE-170893-MS, 2014, https://doi.org/10.2118/170893-MS.

6. Caers J., Modeling uncertainty in the Earth sciences, Wiley-Blackwell, 2011.

7. Kaleta V., van Essen G., van Doren J. et al., Coupled static/dynamic modeling for improved uncertainty handling, SPE-154400-MS, 2012, https://doi.org/10.2118/154400-MS.


8. Chong E., Wan Mohamad W.N., Rae S. et al., Integrated Static and dynamic modelling workflow for improved history matching and uncertainty modelling, SPE-176097-MS, 2015, https://doi.org/10.2118/176097-MS.

The existing methods for history matching (HM) in hydrodynamic modeling do not meet requirements for geologically plausible model. That significantly affects the quality and predictive ability of this models and hence the success of investment decisions. In this paper, the method of automatic HM is presented. The method provides geological parameters control with preserving of identified petrophysical and geological uncertainty that significantly affect reservoir development process. Firstly, based on measured data and data from fields-analogues, realistic limits for each parameter and relationship in model were identified. Further, in order to provide the geologically plausible simulation model within a given geological concept, variation of one parameter or relationship during HM leads to variation of others parameters related to it within previously identified uncertainty range. For example, changes in parameters for porosity calculations leads to changes in parameters for permeability calculations, other model characteristics which related to permeability and so on. Iterative algorithm «evolution strategy» is used for automatic HM. During HM objective function based on mismatch of calculated and historical data for 50 well of sector model of one of the oil fields is minimized. As a result, number adapted models were obtained that demonstrate a good quality of HM. Based on these adapted simulation models forecasting of wells’ working parameters is made with taking into account the uncertainty of the initial data and geological characteristics. The distinctive feature of proposed method is the rejection of single deterministic relationships between petrophysical and geological parameters in favor of variations of these parameters within identified uncertainty range. This method allows speeding up HM and providing control of geological realism of simulation models. As a result, confidence in forecasting based on a set of adapted and geologically plausible models is increased.

References

1. Hajizadeh Y., Christie M., Demyanov V., Comparative study of novel population-based optimization algorithms for history matching and uncertainty quantification: PUNQ-S3 revisited, SPE-136861-MS, 2010, https://doi.org/10.2118/136861-MS.

2. Rwechungura R., Dadashpour M., Advanced history matching techniques reviewed, SPE-142497-MS, 2011, https://doi.org/10.2118/142497-MS.

3. Cancelliere M., Verga F., Viberti D., Benefits and limitations of assisted history matching, SPE-146278-MS, 2011, https://doi.org/10.2118/146278-MS.

4.В  D.J. Schiozer, Almeida Netto S.L., Ligero E.L., Maschio C., Integration of history matching and uncertainty analysis, Journal of Canadian Petroleum Technology, 2005, V. 44, no. 7, https://doi.org/10.2118/05-07-02.

5. Yeh Tzu-Hao et al., Reservoir uncertainty quantification using probabilistic history matching workflow, SPE-170893-MS, 2014, https://doi.org/10.2118/170893-MS.

6. Caers J., Modeling uncertainty in the Earth sciences, Wiley-Blackwell, 2011.

7. Kaleta V., van Essen G., van Doren J. et al., Coupled static/dynamic modeling for improved uncertainty handling, SPE-154400-MS, 2012, https://doi.org/10.2118/154400-MS.


8. Chong E., Wan Mohamad W.N., Rae S. et al., Integrated Static and dynamic modelling workflow for improved history matching and uncertainty modelling, SPE-176097-MS, 2015, https://doi.org/10.2118/176097-MS.


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