Nowadays digitalization is a global technological trend and extends to overall environment. Oil companies are not exception and continue working hard to digitalize processes. The constant growth of the volume of information on fields entails an increase in the time required for analyzing data in order to make a decision on field development management. A software module was developed that enables comprehensive data analysis to plan and assess the effectiveness of non-stationary waterflooding in the field which includes determining the optimal half-cycle period and calculating the actual and predicted effects from the use of non-stationary waterflooding. The software module is implemented based on Python and integrated with software for designing and monitoring field development processes, which allows to easily select the analyzed area and load information on wells, blocks, or waterflooding cells either directly from the software or as a well list. Based on the downloaded data, the recommended period of temporary shutdown of injection for a well/groups of wells is calculated, the forecast effect of this shutdown (reduction of unproductive withdrawals) is calculated and effective planning of the well workover program is carried out for the reservoir pressure maintenance fund at the fields of Gazprom Neft Companу Group. The use of this software module enables to automate routine operations for collecting and analyzing data to make faster and better decisions on field development management.

References

1. Surguchev M.L., Ob uvelichenii nefteotdachi neodnorodnykh plastov (On increasing oil recovery from heterogeneous formations), Proceedings of VNII, 1959, V. 19, pp. 102-110.

2. Abramova L.Yu., Metamodels of neurolinguistic programming using exceptions as an example (In Russ.), Innovatsii. Nauka. Obrazovanie, 2022, no. 51, pp. 2112-2117.

3. Aleksashina A.A., Information technologies for solving linear programming problems (In Russ.), Sovremennaya shkola Rossii. Voprosy modernizatsii, 2022, no. 6(43), pp. 78-79.

4. Bazilevskiy M.P., Method for the M parameter determination in 0-1 mixed-integer linear programming problem for subset selection in linear regression (In Russ.), Vestnik Tekhnologicheskogo universiteta, 2022, V. 25, no. 2, pp. 62-66, DOI: https://doi.org/10.55421/1998-7072_2022_25_2_62

5. Zhornyak A.G., Morozova T.A., Specialized Python(x,y) distribution of the Python programming language for scientific and engineering computing (In Russ.), Nauchno-tekhnicheskiy vestnik Povolzh’ya, 2022, no. 7, pp. 39-42.

6. Willhite G.P., Waterflooding, SPE Textbook Series, 1986.