Approbation of MLR and CRMIP methods in research of well interference

UDK: 622.276.2.038
DOI: 10.24887/0028-2448-2020-8-58-62
Key words: well interference, capacitance-resistance model injector-producer pair based representation (CRMIP), multivariate linear regression (MLR), numerical modelling
Authors: S.V. Bukhmastova (RN-BashNIPIneft LLC, RF, Ufa), R.R. Fakhreeva (RN-BashNIPIneft LLC, RF, Ufa), Yu.A. Pityuk (RN-BashNIPIneft LLC, RF, Ufa), A.Ya. Davletbaev (RN-BashNIPIneft LLC, RF, Ufa), T.P. Azarova (Bashneft PJSC, RF, Ufa), D.V. Farger (Bashneft PJSC, RF, Ufa), R.F. Yakupov (Bashneft-Dobycha LLC, RF, Ufa)
Results of implementation and approbation of well interference methods using field data based on several approach for well interference analysis have been discussed. The software RN-GDIS contains implemented prototypes of software modules including a capacitance-resistance model injector-producer pair based representation (CRMIP) and a multivariate linear regression method (MLR). Field data is required as input data for the software modules. Further, in order to quantify the well interference, the optimization problem is solved and the interaction coefficients are calculated. Coefficients obtained from implemented methods are converted into a single response space. The calculated answers are generalized in a summary table. Using this summary table the decision about the presence or absence of interaction between wells is made. The accuracy of the decision depends on the results of combining field and calculated data.
The developed models were approbated on synthetic data obtained using reservoir simulation model in corporate hydrodynamic simulation tool
RN-KIM. Data preprocessing is conducted before field data analysis. It includes algorithms for initial data reduction to a unifying time array, taking into account the discreteness of measurements and the data type. The CRMIP and MLR methods displayed satisfactory convergence with the results of reservoir simulation, and a good agreement was obtained between the results of field data well interference analysis and the expert assessments of well test specialists.
The results of well interference can be used for setting up reservoir simulation models, interpreting well test taking into account the surrounding wells, it will improve the efficiency of well operation management and reduce the risks of gas, oil and water shows during side-tracking of wells.
References
1. Davletbaev A., Zhilko E., Islamov R. et al., Features of gas well testing in reservoir with low permeability (In Russ.), SPE-176704-RU, 2015, http://dx.doi.org/10.2118/176704-RU
2. Mal'tsev V.V., Asmandiyarov R.N., Baykov V.A. et al., Testing of auto hydraulic-fracturing growth of the linear oilfield development system of Priobskoye oil field (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 5, pp. 70–73.
3. Asalkhuzina G.F., Davletbaev A.Ya., Khabibullin I.L., Akhmetova R.R., On the selection of suitable operate durations for injection tests in low permeability reservoirs (In Russ.), Vestnik Tyumenskogo gosudarstvennogo universiteta, 2020, no. 1 (21), pp. 135–149, DOI: 10.21684/2411-7978-2020-6-1-135-149
4. Asalkhuzina G.F., Bikkinina A.G., Davletbaev A.Ya., Kostrigin I.V., Implementation of well test business processes automation in RN-KIN software by the example of RN-Yuganskneftegas LLC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2020, no. 2, pp. 94–98, DOI: 10.24887/0028-2448-2020-2-94-98
5. Dinh A., Tiab D., Inferring interwell connectivity from well bottomhole-pressure fluctuations in waterfloods, SPE-106881-PA, 2008.
6. Sayyafzadeh M., Pourafshary P., Haghighi M., Rashidi F., Application of transfer functions to model water injection in hydrocarbon reservoir, Journal of Petroleum Science and Engineering, 2011, V. 78, no. 1, pp. 139–148.
7. De Holanda R.W., Capacitance resistance model in a control systems framework: a tool for describing and controlling waterflooding reservoirs: Master's thesis, Texas A & M University, 2015. – 156 p.
8. Yousef A.A., Gentil P.H., Jensen J.L., Lake L.W., A capacitance model to infer interwell connectivity from production and injection rate fluctuations, SPE-95322-PA, 2006.
9. Sayarpour M., Development and application of capacitance-resistive models to water/CO2 floods, Texas: University of Texas, 2008, 218 p.
10. Pichugin O.N., Sannikov I.N., Nikiforov S.V., The forecast of hydraulic fracturing on the basis of the problem-oriented approach (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 5, pp. 88–91.
11. Bukhmastova S.V., Fakhreeva R.R., Pityuk Yu.A., Development of an approach for the numerical analysis of well interference (In Russ.), SPE-196848-RU, 2019.
12. Jensen J.L., Lake L.W., Corbett P.W.M., Goggin D.J., Statistics for petroleum engineers and geoscientists, Upper Saddle River, 1997, 390 p.
13. Bunday B., Basic optimization methods, Edward Arnold, London, 1994, 136 p.
14. Bakhrushin V.E., Methods for evaluating the characteristics of nonlinear statistical relationships (In Russ.), Sistemnye tekhnologii, 2011, V. 73, no. 2, pp. 9–14.
15. Baykov V.A., Badykov I.Kh., Borshchuk O.S., Digital experimentation reservoir laboratory (In Russ.), Nauchno-tekhnicheskiy vestnik OAO “NK “Rosneft'”, 2012, no. 3, pp. 43–47.
16. Aslanyan A., Ganiev B., Lutfullin A. et al., Assessing efficiency of multiwell retrospective testing MRT in analysis of cross-well interference and prediction of formation and bottom-hole pressure dynamics, SPE-196839-MS, 2019, https://doi.org/10.2118/196839-RU.
Results of implementation and approbation of well interference methods using field data based on several approach for well interference analysis have been discussed. The software RN-GDIS contains implemented prototypes of software modules including a capacitance-resistance model injector-producer pair based representation (CRMIP) and a multivariate linear regression method (MLR). Field data is required as input data for the software modules. Further, in order to quantify the well interference, the optimization problem is solved and the interaction coefficients are calculated. Coefficients obtained from implemented methods are converted into a single response space. The calculated answers are generalized in a summary table. Using this summary table the decision about the presence or absence of interaction between wells is made. The accuracy of the decision depends on the results of combining field and calculated data.
The developed models were approbated on synthetic data obtained using reservoir simulation model in corporate hydrodynamic simulation tool
RN-KIM. Data preprocessing is conducted before field data analysis. It includes algorithms for initial data reduction to a unifying time array, taking into account the discreteness of measurements and the data type. The CRMIP and MLR methods displayed satisfactory convergence with the results of reservoir simulation, and a good agreement was obtained between the results of field data well interference analysis and the expert assessments of well test specialists.
The results of well interference can be used for setting up reservoir simulation models, interpreting well test taking into account the surrounding wells, it will improve the efficiency of well operation management and reduce the risks of gas, oil and water shows during side-tracking of wells.
References
1. Davletbaev A., Zhilko E., Islamov R. et al., Features of gas well testing in reservoir with low permeability (In Russ.), SPE-176704-RU, 2015, http://dx.doi.org/10.2118/176704-RU
2. Mal'tsev V.V., Asmandiyarov R.N., Baykov V.A. et al., Testing of auto hydraulic-fracturing growth of the linear oilfield development system of Priobskoye oil field (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 5, pp. 70–73.
3. Asalkhuzina G.F., Davletbaev A.Ya., Khabibullin I.L., Akhmetova R.R., On the selection of suitable operate durations for injection tests in low permeability reservoirs (In Russ.), Vestnik Tyumenskogo gosudarstvennogo universiteta, 2020, no. 1 (21), pp. 135–149, DOI: 10.21684/2411-7978-2020-6-1-135-149
4. Asalkhuzina G.F., Bikkinina A.G., Davletbaev A.Ya., Kostrigin I.V., Implementation of well test business processes automation in RN-KIN software by the example of RN-Yuganskneftegas LLC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2020, no. 2, pp. 94–98, DOI: 10.24887/0028-2448-2020-2-94-98
5. Dinh A., Tiab D., Inferring interwell connectivity from well bottomhole-pressure fluctuations in waterfloods, SPE-106881-PA, 2008.
6. Sayyafzadeh M., Pourafshary P., Haghighi M., Rashidi F., Application of transfer functions to model water injection in hydrocarbon reservoir, Journal of Petroleum Science and Engineering, 2011, V. 78, no. 1, pp. 139–148.
7. De Holanda R.W., Capacitance resistance model in a control systems framework: a tool for describing and controlling waterflooding reservoirs: Master's thesis, Texas A & M University, 2015. – 156 p.
8. Yousef A.A., Gentil P.H., Jensen J.L., Lake L.W., A capacitance model to infer interwell connectivity from production and injection rate fluctuations, SPE-95322-PA, 2006.
9. Sayarpour M., Development and application of capacitance-resistive models to water/CO2 floods, Texas: University of Texas, 2008, 218 p.
10. Pichugin O.N., Sannikov I.N., Nikiforov S.V., The forecast of hydraulic fracturing on the basis of the problem-oriented approach (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 5, pp. 88–91.
11. Bukhmastova S.V., Fakhreeva R.R., Pityuk Yu.A., Development of an approach for the numerical analysis of well interference (In Russ.), SPE-196848-RU, 2019.
12. Jensen J.L., Lake L.W., Corbett P.W.M., Goggin D.J., Statistics for petroleum engineers and geoscientists, Upper Saddle River, 1997, 390 p.
13. Bunday B., Basic optimization methods, Edward Arnold, London, 1994, 136 p.
14. Bakhrushin V.E., Methods for evaluating the characteristics of nonlinear statistical relationships (In Russ.), Sistemnye tekhnologii, 2011, V. 73, no. 2, pp. 9–14.
15. Baykov V.A., Badykov I.Kh., Borshchuk O.S., Digital experimentation reservoir laboratory (In Russ.), Nauchno-tekhnicheskiy vestnik OAO “NK “Rosneft'”, 2012, no. 3, pp. 43–47.
16. Aslanyan A., Ganiev B., Lutfullin A. et al., Assessing efficiency of multiwell retrospective testing MRT in analysis of cross-well interference and prediction of formation and bottom-hole pressure dynamics, SPE-196839-MS, 2019, https://doi.org/10.2118/196839-RU.


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