Application of a domestic system of constant pressure and temperature monitoring in extended-reach wells to solve the problems of the Ob-Taz Bay gas field
UDC: 622.276.1/.4(571.1/.5)
DOI: -
Authors:
TIKHOMIROV DENIS V.
1,
SHARIFULLIN ILDAR F.1,
VALEEV RADIY D.1,
SOLOVYEV TIMUR I.1,
ARBUZOV ANDREY A.2,
NAGIMOV VENER M.2
1 RusGasAlliance, Moscow, Russia
2 NEOWELL, Kazan, Russia
Keywords: domestic pressure and temperature permanent monitoring system (DPTMS), gas reservoir, extended-reach drilling (ERD) well, Ob-Taz Bay, artificial intelligence (AI), horizontal well, novel approach, inflow profile
Annotation:
The authors of the article present the successful experience of application of domestic pressure and temperature permanent monitoring system (DPTMS) for solving the key problems of a gas field development in the Ob-Taz Bay area on the example of horizontal production extended-reach drilling (ERD) well. The approach was successfully tested in a gas reservoir within the Cenomanian formation. The core concept of the system application involves the integration of continuous monitoring of thermobaric conditions using domestic sensors with comprehensive data analysis of reservoir management. It enables real-time monitoring of complex physical processes within the reservoir – well system, such as reservoir pressure decrease, changes of well-reservoir contact quality, variations of hydraulic conductivity and the advancement of dynamic water fronts as well as wells interference. The comprehensive methodology for addressing field development challenges using this system includes: the continuous acquisition of pressure and temperature data under various well operational modes and the performance of well-testing operations to assess wells and reservoir productivity. The results of "DPTMS" system implementation in real ERW gas wells confirmed its high efficiency for production optimization and reservoir surveillance. A complete set of relevant well and reservoir parameters was obtained, enabling the prompt adjustment of wells operational modes without the necessity of conducting conventional well testing. The developed approach has been implemented into standard practice and is used for continuous field development monitoring. Furthermore, the potential for its further enhancement through the application of artificial intelligence technologies for predicting reservoir behavior is currently under consideration.
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