Data & Analytics

Technique Blends Dimensionless Numbers and Data Mining To Predict Recovery Factors

This study shows that dimensionless numbers, together with data-mining techniques, can predict field behavior in terms of recovery factor for sparse data sets.

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Fig. 1—A scatter plot for dimensionless numbers according to K-means clustering. ORF=oil recovery factor; Npc=capillary number; Ng=gravity number; Dn=density number; R1=aspect ratio.

Using attributes from a database of 395 deepwater Gulf of Mexico oil fields, a set of dimensionless numbers is calculated that helps in scaling attributes for all the oil fields. On the basis of these dimensionless numbers, various data-mining techniques are used to classify the oil fields. Subsequently, partial-least-square (PLS) regression is used to relate the dimensionless numbers to the recovery factor. This study shows that dimensionless numbers, together with data-mining techniques, can predict field behavior in terms of recovery factor for sparse data sets.

Introduction

The digitization of information and the rise of inexpensive sensor technologies have ushered in a new era of computing in which acquired data are used to show hidden patterns and trends.

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