Summary
Determination of relative permeability data is required for almost all
calculations of fluid flow in petroleum reservoirs. Water/oil relative
permeability data play important roles in characterizing the simultaneous
two-phase flow in porous rocks and predicting the performance of immiscible
displacement processes in oil reservoirs. They are used, among other
applications, for determining fluid distributions and residual saturations,
predicting future reservoir performance, and estimating ultimate recovery.
Undoubtedly, these data are considered probably the most valuable information
required in reservoir simulation studies. Estimates of relative permeability
are generally obtained from laboratory experiments with reservoir core samples.
In the absence of the laboratory measurement of relative permeability data,
developing empirical correlations for obtaining accurate estimates of relative
permeability data showed limited success, and proved difficult, especially for
carbonate reservoir rocks.
Artificial-neural-network (ANN) technology has proved successful and useful
in solving complex structured and nonlinear problems. This paper presents a new
modeling technology to predict accurately water/oil relative permeability using
ANN. The ANN models of relative permeability were developed using experimental
data from waterflood-core-tests samples collected from carbonate reservoirs of
giant Saudi Arabian oil fields. Three groups of data sets were used for
training, verification, and testing the ANN models. Analysis of results of the
testing data set show excellent agreement with the experimental data of
relative permeability. In addition, error analyses show that the ANN models
developed in this study outperform all published correlations.
The benefits of this work include meeting the increased demand for
conducting special core analysis (SCAL), optimizing the number of laboratory
measurements, integrating into reservoir simulation and reservoir management
studies, and providing significant cost savings on extensive lab work and
substantial required time.
© 2009. Society of Petroleum Engineers
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History
- Original manuscript received:
16 September 2007
- Meeting paper published:
4 September 2007
- Revised manuscript received:
14 April 2008
- Manuscript approved:
10 June 2008
- Published online:
2 March 2009
- Version of record:
26 February 2009
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