Mechanisms for automated modeling of economic indicators of oil and gas fields development in investment projects

UDK: 338.45:622.276
DOI: 10.24887/0028-2448-2021-1-8-11
Key words: intelligent technologies, knowledge engineering, artificial intelligence theory, situational control theory, procedural knowledge
Authors: Yu.G. Bogatkina (Oil and Gas Research Institute of RAS, RF, Moscow)

A modern assessment of the technical and economic efficiency of an oil and gas project involves the construction of a certain economic and mathematical model of calculation, as well as an analysis of project criteria based on a variety of forecast economic indicators for the developed formations and the field as a whole. The automated system developed for this purpose served as the basis for theoretical and applied research in the field of economic modeling and modern information technologies. The article shows that with the help of modern information technologies it is possible to represent formalized knowledge (facts), the truth or falsity of which can be proved. In particular, these methods can be used in the digital economy of subsoil use. At the same time, it is supposed to analyze the processed information on the options for field development in order to solve the problem of synthesizing computational algorithms. Involvement of "systems engineers" in calculations significantly reduces the modeling process. The knowledge bases have been developed over the past two decades and are based on the experience of the technical and economic assessment of oil and gas fields both in our country and abroad. The system allows forecasting the technical and economic indicators of the study and development of hydrocarbon objects, taking into account various tax mechanisms, as well as assessing the value of deposits and the effectiveness of their development using fuzzy methods for assessing the risks of investment forecasts. It provides prompt and high-quality performance of technical and economic calculations for numerous options and sub-options with the choice of the optimal solution that determines the strategy and forecast for the development of oil production with different sources of funding. This development can be a good addition to the already existing software systems for the technical and economic assessment of the effectiveness of the development of oil and gas fields. It is relevant that the bipartite graphs that are part of the developed automated system make it possible to visually enter and correct technical and economic information on field development options.

References

1.  Ponomareva I.A, Bogatkina Yu.G., Eremin N.A., Kompleksnaya ekonomicheskaya otsenka mestorozhdeniy uglevodorodnogo syr'ya v investitsionnykh proektakh (A comprehensive economic evaluation of hydrocarbon fields in investment projects), Moscow: Nauka Publ., 2006, 134 p. 

2. Bogatkina Yu.G., Eremin N.A., Intelligent modeling technologies calculation of economic indicators for eval uation oil and gas deposits (In Russ.), Izvestiya Tul'skogo gosudarstvennogo universiteta. Nauki o Zemle, 2019, no. 3, pp. 344–355.

3. Dmitrievskiy A.N., Eremin N.A., Bogatkina Yu.G., Sardanashvili O.N., Assessment of technical and economic efficiency of investment projects of development of oil and gas deposits based on applica tion of fuzzy logic (In Russ.), Izvestiya Tul'skogo gosudarstvennogo universiteta. Nauki o Zemle, 2019, no. 3, pp. 340–348.

4. Bogatkina Yu.G., Stepankina O.A., Structure of intelligent interface in "Graph" logical system (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2015, no. 1, pp. 25–30.

5. Bashmakov A.I, Bashmakov I.A., Intellektual'nye informatsionnye tekhnologii (Intelligent information technology), Moscow: Publ. of Bauman University, 2005, 304 p.

6. Berezhnaya E.V., Berezhnoy V.I., Matematicheskie metody modelirovaniya ekonomicheskikh sistem (Mathematical methods for modeling economic systems), Moscow: Finansy i statistika Publ., 2006, 432 p.

7. Vagin V.N., Deduktsiya i obobshchenie v sistemakh prinyatiya resheniy (Deduction and generalization in decision-making systems), Moscow: Nauka Publ., 1988, 384 p.

8. Dunaev V.F., Shpakov V.A., Epifanova N.P. et al., Ekonomika predpriyatiy neftyanoy i gazovoy promyshlennosti (Economics of oil and gas industry), Moscow: Publ. of Gubkin Univetsity, 2008, 305 p. 

9. Konoplyanik A.A., Osnovnye vidy i usloviya finansirovaniya investitsionnykh proektov v neftegazodobyvayushchey promyshlennosti (The main types and conditions of financing investment projects in the oil and gas industry), Moscow: Publ. of Gubkin Univetsity, 2009, 62 p.

10. Pospelov G.S., Iskusstvennyy intellekt – osnova novoy informatsionnoy tekhnologii (Artificial intelligence is the basis of new information technology), Moscow: Nauka Publ., 1988, 280 p.

11. Trakhtengerts E.A, Stepin Yu.P., Andreev A.F., Komp'yuternye metody podderzhki prinyatiya upravlencheskikh resheniy v neftegazovoy promyshlennosti (Computer methods for supporting management decisions in the oil and gas industry), Moscow: SINTEG Publ., 2005, 592 p.

A modern assessment of the technical and economic efficiency of an oil and gas project involves the construction of a certain economic and mathematical model of calculation, as well as an analysis of project criteria based on a variety of forecast economic indicators for the developed formations and the field as a whole. The automated system developed for this purpose served as the basis for theoretical and applied research in the field of economic modeling and modern information technologies. The article shows that with the help of modern information technologies it is possible to represent formalized knowledge (facts), the truth or falsity of which can be proved. In particular, these methods can be used in the digital economy of subsoil use. At the same time, it is supposed to analyze the processed information on the options for field development in order to solve the problem of synthesizing computational algorithms. Involvement of "systems engineers" in calculations significantly reduces the modeling process. The knowledge bases have been developed over the past two decades and are based on the experience of the technical and economic assessment of oil and gas fields both in our country and abroad. The system allows forecasting the technical and economic indicators of the study and development of hydrocarbon objects, taking into account various tax mechanisms, as well as assessing the value of deposits and the effectiveness of their development using fuzzy methods for assessing the risks of investment forecasts. It provides prompt and high-quality performance of technical and economic calculations for numerous options and sub-options with the choice of the optimal solution that determines the strategy and forecast for the development of oil production with different sources of funding. This development can be a good addition to the already existing software systems for the technical and economic assessment of the effectiveness of the development of oil and gas fields. It is relevant that the bipartite graphs that are part of the developed automated system make it possible to visually enter and correct technical and economic information on field development options.

References

1.  Ponomareva I.A, Bogatkina Yu.G., Eremin N.A., Kompleksnaya ekonomicheskaya otsenka mestorozhdeniy uglevodorodnogo syr'ya v investitsionnykh proektakh (A comprehensive economic evaluation of hydrocarbon fields in investment projects), Moscow: Nauka Publ., 2006, 134 p. 

2. Bogatkina Yu.G., Eremin N.A., Intelligent modeling technologies calculation of economic indicators for eval uation oil and gas deposits (In Russ.), Izvestiya Tul'skogo gosudarstvennogo universiteta. Nauki o Zemle, 2019, no. 3, pp. 344–355.

3. Dmitrievskiy A.N., Eremin N.A., Bogatkina Yu.G., Sardanashvili O.N., Assessment of technical and economic efficiency of investment projects of development of oil and gas deposits based on applica tion of fuzzy logic (In Russ.), Izvestiya Tul'skogo gosudarstvennogo universiteta. Nauki o Zemle, 2019, no. 3, pp. 340–348.

4. Bogatkina Yu.G., Stepankina O.A., Structure of intelligent interface in "Graph" logical system (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2015, no. 1, pp. 25–30.

5. Bashmakov A.I, Bashmakov I.A., Intellektual'nye informatsionnye tekhnologii (Intelligent information technology), Moscow: Publ. of Bauman University, 2005, 304 p.

6. Berezhnaya E.V., Berezhnoy V.I., Matematicheskie metody modelirovaniya ekonomicheskikh sistem (Mathematical methods for modeling economic systems), Moscow: Finansy i statistika Publ., 2006, 432 p.

7. Vagin V.N., Deduktsiya i obobshchenie v sistemakh prinyatiya resheniy (Deduction and generalization in decision-making systems), Moscow: Nauka Publ., 1988, 384 p.

8. Dunaev V.F., Shpakov V.A., Epifanova N.P. et al., Ekonomika predpriyatiy neftyanoy i gazovoy promyshlennosti (Economics of oil and gas industry), Moscow: Publ. of Gubkin Univetsity, 2008, 305 p. 

9. Konoplyanik A.A., Osnovnye vidy i usloviya finansirovaniya investitsionnykh proektov v neftegazodobyvayushchey promyshlennosti (The main types and conditions of financing investment projects in the oil and gas industry), Moscow: Publ. of Gubkin Univetsity, 2009, 62 p.

10. Pospelov G.S., Iskusstvennyy intellekt – osnova novoy informatsionnoy tekhnologii (Artificial intelligence is the basis of new information technology), Moscow: Nauka Publ., 1988, 280 p.

11. Trakhtengerts E.A, Stepin Yu.P., Andreev A.F., Komp'yuternye metody podderzhki prinyatiya upravlencheskikh resheniy v neftegazovoy promyshlennosti (Computer methods for supporting management decisions in the oil and gas industry), Moscow: SINTEG Publ., 2005, 592 p.


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