Open AccessOil Reservoir Classification by Geological and Production Data Using Unsupervised Machine Learning Algorithm
Author(s) -
D. V. Kurganov
Publication year - 2020
Publication title -
vestnik novosibirskogo gosudarstvennogo universiteta. seriâ: informacionnye tehnologii/vestnik novosibirskogo gosudarstvennogo universiteta. seriâ: informacionnye tehnologii v obrazovanii
Language(s) - English
Resource type - Journals
eISSN - 2410-0420
pISSN - 1818-7900
DOI - 10.25205/1818-7900-2020-18-1-27-35
Subject(s) - porosity , saturation (graph theory) , oil production , permeability (electromagnetism) , algorithm , production (economics) , computer science , petroleum engineering , artificial intelligence , data mining , geology , mathematics , geotechnical engineering , chemistry , combinatorics , biochemistry , membrane , economics , macroeconomics
In machine learning, k-means unsupervised model is used for classification analysis. In this paper k-means model is applied for productivity prediction of giant Western Siberian oilfield. An essential condition for method’s application is availability of digital databases with representative results. Complex method allows combine different reservoir and production parameters: rates, porosity, saturation, frac parameters etc. The method can be particularly useful in complicated reservoirs, e.g. in dual porosity ones, where the relationship between formation parameters (permeability, porosity, saturation) and production rates is unclear and cannot be set by traditional development analysis, particularly in frac environment.