Identification of unplanned shutdowns for buildup tests in real time

UDK: 622.276.5.001.5
DOI: 10.24887/0028-2448-2020-2-32-35
Key words: well tests, pressure buildup curve, unplanned shutdown of well, automation, real time
Authors: Yu.A. Pityuk (RN-BashNIPIneft LLC, RF, Ufa), A.F. Kunafin (RN-BashNIPIneft LLC, RF, Ufa), A.R. Bairamgalin (RN-BashNIPIneft LLC, RF, Ufa), A.Ya. Davletbaev (RN-BashNIPIneft LLC, RF, Ufa), A.M. Toloka (Sphere-Visual Lab LLC, RF, Moscow), E.V. Makarkhin (Sphere-Visual Lab LLC, RF, Moscow), T.P. Azarova (Bashneft Oil Company PJSC, RF, Ufa), D.V. Farger (Bashneft Oil Company PJSC, RF, Ufa), A.S. Krivylyak (Bashneft-Dobycha LLC, RF, Ufa), S.A. Zyleva (Bashneft-Dobycha LLC, RF, Ufa)

For effective monitoring of oil production wells and operational monitoring of field development, it is necessary to constantly receive and update information on the parameters of the reservoir and wells. One of the sources of such information is the results of interpretation and analysis of well tests. To obtain and process well dynamic data in real time at the pilot site in the framework of Digital Field project of Bashneft PJSOC, all production wells with an electric centrifugal pump were equipped with a thermomanometers.

Nowadays, the authors of the present work have developed a functional module «Online well test», which is part of the corporate high-tech software line, to automate buildup tests in real time. Since May 2019, the module «Online well test» has been brought into pilot production and is being tested on field data for mechanized wells. In real time, the automated system identifies shutdowns of production wells and processes well dynamic data. Then, on the basis of data analysis algorithms, the system gives the estimated duration of well test, planned oil losses, as well as recommendations on expediency and inexpediency of well test conducting with the indicating the reasons. Based on the received information, the user of the module «Online well test» makes a decision on the feasibility of well test conducting at a stopped well. After the well test is completed, the field data is automatically loaded into the «RN-KIN» system for the final interpretation.

The implemented tool allows you to optimize and/or expand the annual program of well tests by including unplanned controlled shutdowns of the wells with expediency, and to increase the number and coverage of well tests. Automatic search for unplanned technological shutdowns of mechanized wells can reduce losses in oil production due to well test conducting in these wells. Real-time notifications for expediency of well test conducting allow you to quickly make management decisions for wells with technological stops, while users of the module «Online well test» do not require knowledge of the theory and interpretation skills of well tests. This allows you to make reasonable decisions in the shortest possible time in real time.

References

1. Afanas'ev I.S., Sergeychev A.V., Asmandiyarov R.N. et al., Automatic well test data processing: a time series wavelet analysis approach (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 11, pp. 34–37.

2. Liu Y., Interpreting pressure and flow rate data from permanent downhole gauges using data mining approaches: Dissertation for the degree of doctor of philosophy, 2013, 243 p.

3. Grigor'ev I.M., Effektivnye algoritmy dlya avtomatizatsii analiza i interpretatsii gidrodinamicheskikh issledovaniy skvazhin (Effective algorithms for automating the analysis and interpretation of well tests): thesis of candidate of technical science, Izhevsk, 2014.

4. Rochev A.N., Povyshenie informativnosti gidrodinamicheskikh issledovaniy skvazhin (Improving the informativeness of hydrodynamic studies of wells): thesis of candidate of technical science, Ukhta, 2004.

5. Chuan T., Machine learning approaches for permanent downhole gauge data interpretation: Dissertation for the degree of doctor of philosophy, 2018.

6. Athichanagorn S., Home R.N., Automatic parameter estimation from well test data using artificial neural network, SPE-30556-MS, 1995.

7. Kotezhekov V., Margarit A., Pustovskikh A., Sitnikov A., Development of automatic system for decline analysis (In Russ.), SPE-187755-RU, 2017.

8. Abbaszadeh M., Kamal M.M., Automatic type-curve matching for well test analysis, SPE-16443-PA, 1988.

9. Pashali A.A., Aleksandrov M.A., Kliment'ev A.G. et al., Automatization of collecting and preparation of telemetry data for well testing using ''virtual flowmeter'' (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 11, pp. 60–63.

10. Morozovskiy N., Mel'nikov S., Krichevskiy V., Feoktistov R., Pressure depletion optimization and increase of oil production by well test and decline analysis (In Russ.), SPE-176565-RU, 2015.

11. Ishkin D.Z., Nuriev R.I., Davletbaev A.Ya. et al., Decline-analysis/short build-up welltest analysis of low permeability gas reservoir (In Russ.), SPE-181974-RU, 2016.

12. Earlougher R.C. Jr., Advances in well test analysis, SPE Monograph Series, 1977, V. 5, 264 p.

For effective monitoring of oil production wells and operational monitoring of field development, it is necessary to constantly receive and update information on the parameters of the reservoir and wells. One of the sources of such information is the results of interpretation and analysis of well tests. To obtain and process well dynamic data in real time at the pilot site in the framework of Digital Field project of Bashneft PJSOC, all production wells with an electric centrifugal pump were equipped with a thermomanometers.

Nowadays, the authors of the present work have developed a functional module «Online well test», which is part of the corporate high-tech software line, to automate buildup tests in real time. Since May 2019, the module «Online well test» has been brought into pilot production and is being tested on field data for mechanized wells. In real time, the automated system identifies shutdowns of production wells and processes well dynamic data. Then, on the basis of data analysis algorithms, the system gives the estimated duration of well test, planned oil losses, as well as recommendations on expediency and inexpediency of well test conducting with the indicating the reasons. Based on the received information, the user of the module «Online well test» makes a decision on the feasibility of well test conducting at a stopped well. After the well test is completed, the field data is automatically loaded into the «RN-KIN» system for the final interpretation.

The implemented tool allows you to optimize and/or expand the annual program of well tests by including unplanned controlled shutdowns of the wells with expediency, and to increase the number and coverage of well tests. Automatic search for unplanned technological shutdowns of mechanized wells can reduce losses in oil production due to well test conducting in these wells. Real-time notifications for expediency of well test conducting allow you to quickly make management decisions for wells with technological stops, while users of the module «Online well test» do not require knowledge of the theory and interpretation skills of well tests. This allows you to make reasonable decisions in the shortest possible time in real time.

References

1. Afanas'ev I.S., Sergeychev A.V., Asmandiyarov R.N. et al., Automatic well test data processing: a time series wavelet analysis approach (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 11, pp. 34–37.

2. Liu Y., Interpreting pressure and flow rate data from permanent downhole gauges using data mining approaches: Dissertation for the degree of doctor of philosophy, 2013, 243 p.

3. Grigor'ev I.M., Effektivnye algoritmy dlya avtomatizatsii analiza i interpretatsii gidrodinamicheskikh issledovaniy skvazhin (Effective algorithms for automating the analysis and interpretation of well tests): thesis of candidate of technical science, Izhevsk, 2014.

4. Rochev A.N., Povyshenie informativnosti gidrodinamicheskikh issledovaniy skvazhin (Improving the informativeness of hydrodynamic studies of wells): thesis of candidate of technical science, Ukhta, 2004.

5. Chuan T., Machine learning approaches for permanent downhole gauge data interpretation: Dissertation for the degree of doctor of philosophy, 2018.

6. Athichanagorn S., Home R.N., Automatic parameter estimation from well test data using artificial neural network, SPE-30556-MS, 1995.

7. Kotezhekov V., Margarit A., Pustovskikh A., Sitnikov A., Development of automatic system for decline analysis (In Russ.), SPE-187755-RU, 2017.

8. Abbaszadeh M., Kamal M.M., Automatic type-curve matching for well test analysis, SPE-16443-PA, 1988.

9. Pashali A.A., Aleksandrov M.A., Kliment'ev A.G. et al., Automatization of collecting and preparation of telemetry data for well testing using ''virtual flowmeter'' (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 11, pp. 60–63.

10. Morozovskiy N., Mel'nikov S., Krichevskiy V., Feoktistov R., Pressure depletion optimization and increase of oil production by well test and decline analysis (In Russ.), SPE-176565-RU, 2015.

11. Ishkin D.Z., Nuriev R.I., Davletbaev A.Ya. et al., Decline-analysis/short build-up welltest analysis of low permeability gas reservoir (In Russ.), SPE-181974-RU, 2016.

12. Earlougher R.C. Jr., Advances in well test analysis, SPE Monograph Series, 1977, V. 5, 264 p.


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