Technique Blends Dimensionless Numbers and Data Mining To Predict Recovery Factors

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. This method of computing is very efficient in solving inverse problems where parameters affecting system characteristics are not completely known. Hydrocarbon reservoirs provide a classic case of a natural system where engineers have limited control on the design of the system that they work with; thus, they have to rely on indirect measurements to determine properties of the reservoir and use these properties for prediction of future trends. Performance prediction is usually accomplished with either analytical material-balance equations or numerical reservoir simulation. However, both methods use a bottom-up work flow, which suffers from a drawback: the need for accurate representation of subsurface geology. Data mining, on the other hand, provides an alternative top-down intelligent-reservoir-modeling approach, which uses measured reservoir properties as the basis for modeling.

Using publicly available information—including information on geology, geophysics, reserves, production, and infrastructure—the complete paper applies various data-mining and predictive-analytics algorithms to estimate recovery factor. Classical reservoir engineering assumes that recovery factor is dependent on rock properties, fluid properties, geological structures, and mode of production. Instead of using traditional deterministic methods, such as material balance or numerical simulation, this study uses data-driven analytics to estimate the ­recovery factor.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181024, “Recovery-Factor Prediction for Deepwater Gulf of Mexico Oil Fields by Integration of Dimensionless Numbers With Data-Mining Techniques,” by Priyank Srivastava and Xingru Wu, SPE, University of Oklahoma, and Amin Amirlatifi, SPE, Mississippi State University, prepared for the 2016 SPE Intelligent Energy International Conference and Exhibition, Aberdeen, 6–8 September. The paper has not been peer reviewed.
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Technique Blends Dimensionless Numbers and Data Mining To Predict Recovery Factors

01 October 2017

Volume: 69 | Issue: 10

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