Developing New Corey-Based Water/Oil Relative Permeability Correlations for Heavy Oil Systems
Bibliographic record
Abstract
Abstract Water/oil relative permeability data plays an important role in characterizing the simultaneous two-phase flow of fluids in porous media and predicting the performance of water flooding as a means of an immiscible displacement processes in oil reservoirs. Review of literatures indicated extensive experimental studies on two-phase water/oil relative permeability for light oil systems, however, such studies on the effect of various crude oil characteristics and operational factors on water/oil relative permeability in heavy oil systems are limited. In addition, previously developed correlations, such as Corey's equations, are not satisfactory when applied to heavy oil systems. The Objectives of this study were: I) to investigate the effect of temperature, viscosity, flow rate, and pressure on water/oil relative permeability, experimentally and II) to develop a set of new correlations for calculating water/oil relative permeability in which the effects of pressure, viscosity (temperature), and flow rate were incorporated. The experimental results showed that both water and oil relative permeability values are significantly temperature dependent and they increase when temperature increases. The results revealed that relative permeability to oil and water increase with decrease in oil viscosity. Increase in injection flow rate resulted in higher oil relative permeability and lower water relative permeability. The tests results also indicated that relative permeability to oil in water-heavy oil system is almost independent of operating pressure. The experimental data obtained in this study was used to develop new water/oil relative permeability correlations. The comparative evaluation of the new correlations with those developed by Corey showed significant improvement in prediction of water/heavy oil relative permeability. Statistical analysis of the results showed that the new correlations facilitate reliable calculation of water/oil relative permeability values by decreasing the Root Mean Square magnitude from 0.167 and 0.178 to 0.004 and 0.061 for water and oil relative permeability, respectively. In addition, the accuracy of newly developed correlations was tested against three sets of heavy oil experimental data obtained by other researchers. Results of this comparison also showed that water/oil relative permeability predicted by new correlations are in better agreement with experimental data compare to those predicted by Corey's model. Introduction Relative permeability is a crucial empirical parameter in describing the flow of multiple immiscible fluids within a porous medium (Amyx et al. 1960; Frick 1962; Heaviside et al. 1983; Honarpour et al. 1986; Al-Fattah 2003). It is defined as the ratio of the effective permeability of a fluid at a given saturation to the absolute permeability of the rock. Relative permeability data is essential for almost all fluid flow calculations in reservoirs and is utilized extensively in many areas of petroleum engineering such as: determining the residual fluid saturations, calculating the fractional flow and frontal advance, making engineering estimates of productivity, injectivity and ultimate recovery. The data is more particularly used for matching, predicting and optimizing oil and gas reservoir performances through numerical simulations. Relative permeability values are generally obtained from laboratory experiments on reservoir core samples using one of the measurement methods: steady state, unsteady state, or centrifuge techniques. The relative permeability data may also be determined from field data using the production history of a reservoir and its fluid properties. However, this approach is not often practical because it requires the complete production history data and provides average values which are influenced by pressure and saturation gradients, differences in the depletion stage and saturation variations in reservoirs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".