Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Abstract

Selecting a proper enhanced oil recovery (EOR) method for a prospective reservoir is a key factor for successful application of EOR techniques. Reservoir engineers usually refer to screening guidelines to identify potential EOR processes for a given reservoir. However, these guidelines are often too general. In this study, we develop an advanced EOR screening technique based on the statistical analyses with boxplot in combination with some initial deep learning analyses to select the most suitable EOR method for a given mature oil field. At first, a database and the screening guidelines were established by compiling the information of 1,098 EOR projects from various publications in different languages, including Oil and Gas Journal (OGJ) biannual EOR surveys, SPE publications, DOE reports, and Chinese publications, etc. Boxplots were used to detect the special cases for each reservoir/fluid property and to present the graphical screening results. A case study was used to demonstrate that with a simple input of reservoir/fluid information, the proposed procedure could effectively give recommendations for EOR method selection. With the inputs (reservoir and fluid properties) from Vietnam offshore oil fields, the EOR methods recommended by this study are mostly chemical, including polymer and surfactant injection.