Open AccessAdaptive interpretation of gas well deliverability tests with generating data of the IPR curve
Author(s) -
Виктор Леонидович Сергеев,
Nguyen Thac Hoai Phuong,
Александр Игоревич Крайнов
Publication year - 2017
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/803/1/012136
Subject(s) - well test (oil and gas) , flow (mathematics) , a priori and a posteriori , test data , interpretation (philosophy) , experimental data , field (mathematics) , computer science , mathematical optimization , mathematics , statistics , engineering , petroleum engineering , philosophy , geometry , epistemology , pure mathematics , programming language
The paper considers topical issues of improving accuracy of estimated parameters given by data obtained from gas well deliverability tests, decreasing test time, and reducing gas emissions into the atmosphere. The aim of the research is to develop the method of adaptive interpretation of gas well deliverability tests with a resulting IPR curve and using a technique of generating data, which allows taking into account additional a priori information, improving accuracy of determining formation pressure and flow coefficients, reducing test time. The present research is based on the previous theoretical and practical findings in the spheres of gas well deliverability tests, systems analysis, system identification, function optimization and linear algebra. To test the method, the authors used the field data of deliverability tests of two wells, run in the Urengoy gas and condensate field, Tyumen Oblast. The authors suggest the method of adaptive interpretation of gas well deliverability tests with the resulting IPR curve and the possibility of generating data of bottomhole pressure and a flow rate at different test stages. The suggested method allows defining the estimates of the formation pressure and flow coefficients, optimal in terms of preassigned measures of quality, and setting the adequate number of test stages in the course of well testing. The case study of IPR curve data processing has indicated that adaptive interpretation provides more accurate estimates on the formation pressure and flow coefficients, as well as reduces the number of test stages