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The concept of a system for monitoring the reliability and operation of pipelines

UDK: 622.692.4
DOI: 10.24887/0028-2448-2018-9-128-132
Key words: field pipeline, pipeline maintenance, ranking, damage, reliability enhancement, complications, system reliability prediction
Authors: A.V. Arzhilovskiy, A.F. Alferov, R.I. Valiakhmetov, P.V. Vinogradov BashNIPIneft LLC, RF, Ufa), E.B. Danileiko Rosneft Oil Company, RF, Moscow)

The article presents a new approach to the development of information systems on the maintenance of operation of field pipelines. New guidelines for the technological development of the Company - advanced information technology, the transition to digital production, predictive analytics, decision support was adopted as the basis. The main principles of the concept of a new system for monitoring the technical condition of field pipelines (RN-PipeControl) are data gathering, accumulation and analysis of the maximum amount of data generated during the operation of pipelines. The system should provide automated loading and processing of data in a time mode close to real time. Data processing involves the use of machine learning methods, technologies for working with big data, to use emergent approach to the formation of Analytics. The system will solve a wide range of tasks, automate processes and reduce the number of routine operations both at the workshop level, oil-and-gas production department, and at the level of the group companies and the Company as a whole.

The main automated processes are the formation and technical service and repair maintenance programs of pipelines, optimum performance control determination, complications monitoring and prevention, risks management. The formation and implementation of reliability improvement program is an important component of maintaining the operation of pipelines. For reducing the number of failures the program represents various activities on the pipeline fund (reconstruction, diagnostics, capital upgrades). A common approach (both in domestic oil companies and abroad) involves ranking pipelines for assigning them certain activities based on relative criteria (points, expert appraisal on the probability of failure, damage). In the developed approach it is supposed to depart from the current indicators of effectiveness evaluation (specific frequency of gusts, operational costs, and capital costs) and use a single integrated indicator - total cost of ownership (CER). The transition to the CER indicator combined with the use of simulation modeling will allow to predict the accident rate and to evaluate the effectiveness of reliability improvement programs in the short, medium and long term.

References

1. URL: https://www.rosneft.ru/press/news_about/item/189383/

2. Kiefner J.F., A risk management tool for establishing budget priorities, NACE TechEdge Series Program, Houston, Texas, 1997, 10-12 February.

3. Vinogradov P.V., Litvinenko K.V., Valiakhmetov R.I., Bakhtegareeva A.N., Development of a model for ranking field pipelines based on risk assessment in exploitation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 9, pp.

4. Orlov A.I., Prikladnaya statistika (Applied statistics), Moscow: Ekzamen Publ., 2006, 671 p.

5. Butov A.A., Sharov V.D., Makarov V.P., Orlov A.I., The automated system of aviation accidents forecasting and prevention at the organization and performance of flights (In Russ.), Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk, 2012, V. 14, no. 4(2), pp. 380-385.

The article presents a new approach to the development of information systems on the maintenance of operation of field pipelines. New guidelines for the technological development of the Company - advanced information technology, the transition to digital production, predictive analytics, decision support was adopted as the basis. The main principles of the concept of a new system for monitoring the technical condition of field pipelines (RN-PipeControl) are data gathering, accumulation and analysis of the maximum amount of data generated during the operation of pipelines. The system should provide automated loading and processing of data in a time mode close to real time. Data processing involves the use of machine learning methods, technologies for working with big data, to use emergent approach to the formation of Analytics. The system will solve a wide range of tasks, automate processes and reduce the number of routine operations both at the workshop level, oil-and-gas production department, and at the level of the group companies and the Company as a whole.

The main automated processes are the formation and technical service and repair maintenance programs of pipelines, optimum performance control determination, complications monitoring and prevention, risks management. The formation and implementation of reliability improvement program is an important component of maintaining the operation of pipelines. For reducing the number of failures the program represents various activities on the pipeline fund (reconstruction, diagnostics, capital upgrades). A common approach (both in domestic oil companies and abroad) involves ranking pipelines for assigning them certain activities based on relative criteria (points, expert appraisal on the probability of failure, damage). In the developed approach it is supposed to depart from the current indicators of effectiveness evaluation (specific frequency of gusts, operational costs, and capital costs) and use a single integrated indicator - total cost of ownership (CER). The transition to the CER indicator combined with the use of simulation modeling will allow to predict the accident rate and to evaluate the effectiveness of reliability improvement programs in the short, medium and long term.

References

1. URL: https://www.rosneft.ru/press/news_about/item/189383/

2. Kiefner J.F., A risk management tool for establishing budget priorities, NACE TechEdge Series Program, Houston, Texas, 1997, 10-12 February.

3. Vinogradov P.V., Litvinenko K.V., Valiakhmetov R.I., Bakhtegareeva A.N., Development of a model for ranking field pipelines based on risk assessment in exploitation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 9, pp.

4. Orlov A.I., Prikladnaya statistika (Applied statistics), Moscow: Ekzamen Publ., 2006, 671 p.

5. Butov A.A., Sharov V.D., Makarov V.P., Orlov A.I., The automated system of aviation accidents forecasting and prevention at the organization and performance of flights (In Russ.), Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk, 2012, V. 14, no. 4(2), pp. 380-385.



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