Predicting reservoir oil viscosity with numerical correlation equations using
field-measured variables is widely used in the petroleum industry. Most published
correlation equations, however, have never profoundly realized the genuine relationship
between the reservoir oil viscosity and other field-measured parameters. Using the
proposed systematic strategy is an effective solution for achieving a high performance
correlation equation of reservoir oil viscosity.
The proposed strategy begins with creating a large database of pressure-volumetemperature
(PVT) reports and screening all possible erroneous data. The relationship
between the oil viscosity and other field-measured parameters is intensively analyzed by
using theoretical and empirical approaches to determine the influential parameters for
correlating reservoir oil viscosity equations. The alternating conditional expectation
(ACE) algorithm is applied for correlating saturated and undersaturated oil viscosity
equations. The precision of field-measured PVT data is inspected by a data
reconciliation technique in order to clarify the correctness of oil viscosity correlations.
Finally, the performance of the proposed oil viscosity correlation equations is
represented in terms of statistical error analysis functions.
The result of this study shows that reservoir oil density turns out to be the most
effective parameter for correlating both saturated and undersaturated reservoir oil
viscosity equations. Expected errors in laboratory-measured oil viscosity are the main
factors that degrade the efficiency of oil viscosity correlation equations. The proposed
correlation equations provide a reasonable estimate of reservoir oil viscosity; and their
superior performance is more reliable than that of published correlation equations at any
reservoir conditions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3168 |
| Date | 12 April 2006 |
| Creators | Kulchanyavivat, Sawin |
| Contributors | McCain, William D. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 10954548 bytes, electronic, application/pdf, born digital |
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