Inderect Determination of the Reservoir Pressure Using Data Mining

Kharisov, M.N.; Yunusova, E.A.; Maiski, R.A.

doi:10.17122/ntj-oil-2018-3-40-48

Cited by 2 publications

(1 citation statement)

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“…Application of the method of regression analysis with the application of a genetic algorithm enables to refine the value of reservoir pressure based on the results of several sequential well stimulations under conditions of uncertainty in the measurement error of well operation indicators (Figure 3). To analyze the results of the indirect determination of reservoir pressure by changing the operating modes of wells, the authors used an algorithm described in detail in [26].…”

Section: Methodsmentioning

confidence: 99%

Indirect determination of reservoir pressure by changing operating modes of exploration and recovery wells

Grishchenko

Kharisov

akupov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The results obtained in the article enable to draw a conclusion about the sufficient accuracy of assessing the energy state and the possibility of using an algorithm for the indirect determination of reservoir pressure for solving practical problems of geological exploration and geological recovery research in selecting and planning geological and engineering operations and monitoring the work of the base well stock. However, algorithms for indirect determination of reservoir pressure should not fully replace well testing but they can be considered as an additional tool for assessing the reservoir pressure and their results should be analyzed considering the experience of the reservoir development in specific geological conditions.

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Section: Methodsmentioning

confidence: 99%

Indirect determination of reservoir pressure by changing operating modes of exploration and recovery wells

Grishchenko

Kharisov

akupov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Predicting dynamic formation pressure using artificial intelligence methods

Zakharov

Martyushev

Ponomareva

2022

PMI

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Determining formation pressure in the well extraction zones is a key task in monitoring the development of hydrocarbon fields. Direct measurements of formation pressure require prolonged well shutdowns, resulting in underproduction and the possibility of technical problems with the subsequent start-up of wells. The impossibility of simultaneous shutdown of all wells of the pool makes it difficult to assess the real energy state of the deposit. This article presents research aimed at developing an indirect method for determining the formation pressure without shutting down the wells for investigation, which enables to determine its value at any time. As a mathematical basis, two artificial intelligence methods are used – multidimensional regression analysis and a neural network. The technique based on the construction of multiple regression equations shows sufficient performance, but high sensitivity to the input data. This technique enables to study the process of formation pressure establishment during different periods of deposit development. Its application is expedient in case of regular actual determinations of indicators used as input data. The technique based on the artificial neural network enables to reliably determine formation pressure even with a minimal set of input data and is implemented as a specially designed software product. The relevant task of continuing the research is to evaluate promising prognostic features of artificial intelligence methods for assessing the energy state of deposits in hydrocarbon extraction zones.

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Inderect Determination of the Reservoir Pressure Using Data Mining

Cited by 2 publications

References 1 publication

Indirect determination of reservoir pressure by changing operating modes of exploration and recovery wells

Indirect determination of reservoir pressure by changing operating modes of exploration and recovery wells

Predicting dynamic formation pressure using artificial intelligence methods

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