Study on new method for evaluating reservoir formation pressure by wellhead pressure

Wen, Xiaoyong; Yang, Jin; Geng, Shaoyang; Li, Ke

doi:10.1088/1755-1315/384/1/012037

IOP Conf. Ser.: Earth Environ. Sci.

2019

DOI: 10.1088/1755-1315/384/1/012037

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Study on new method for evaluating reservoir formation pressure by wellhead pressure

Xiaoyong Wen

Jin Yang

Shaoyang Geng

et al.

Abstract: Formation pressure is an essential parameter for calculating the dynamic geological reserves, evaluating the development effect of oil and gas fields, conducting the daily dynamic analysis of oil and gas Wells and predicting the dynamic of oil and gas fields. Generally, the calculation of reservoir average formation pressure is to use the pressure in the infinite formation to solve the formation pressure. This paper presents a new method for calculating the pressure at any point of one source and one sink by u… Show more

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“…A new method for calculating pressure at any point of a single source and a single drain using wellhead pressure, tested and actively used at oil fields in China, is described in work [13].…”

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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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mentioning

confidence: 99%

Predicting dynamic formation pressure using artificial intelligence methods

Zakharov

Martyushev

Ponomareva

2022

PMI

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Factor analysis-based deep differentiation of oil deposits in the Ural-Volga region

Gilyazetdinov,

Kuleshova,

Mukhametshin

et al. 2024

jour

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The purpose of the article is to present an algorithm developed on the scientific and methodological foundations of quantitative and qualitative processing of geological and commercial data to implement the procedure for deep identification of deposits. The developed algorithm consists of two levels: the initial stage includes facility identification by tectonic and stratigraphic characteristics resulting in the formation of a number of megagroups of objects. Then they are subjected to deep differentiation using the elements of data factor analysis, which is carried out together with the monitoring of the highly identical objects. The presented approach to solving the problems of field effective grouping is the most effective due to a comprehensive and reasonable assessment of the groups of facilities formed as a result of modeling. The developed algorithm was tested on example of a number of fields associated with terrigenous reservoirs of the Devonian and carboniferous systems of the Volga-Ural oil and gas province. After two calculation stages the percentage of correctly grouped objects averaged 96.8 %, which is a high result. To make the search for analogous objects qualitative and objective eighteen equations have been obtained that combine twenty parameters describing the geological and physical characteristics of productive formations as well as the physico-chemical properties of the fluids saturating them at the sufficient level of reliability. Based on the results of using the developed algorithm for deep identification of deposits, the authors obtained a number of relevant mathematical dependencies between various parameters, graphical distributions of objects in the axes of the main components, which all together enable efficient and systematic search for analogous objects in the deposits of terrigenous reservoirs of the Devonian and carboniferous systems of the Volga-Ural oil and gas province. Besides, the presented identification diagrams enable successful management of the processes of oil recovery within the micro- and macro-levels of facility distribution in the axes of the main components. They also allow to form a list of general recommendations that will contribute to the optimal development of liquid hydrocarbon resources.

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Study on new method for evaluating reservoir formation pressure by wellhead pressure

Cited by 2 publications

References 0 publications

Predicting dynamic formation pressure using artificial intelligence methods

Predicting dynamic formation pressure using artificial intelligence methods

Factor analysis-based deep differentiation of oil deposits in the Ural-Volga region

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