Bibliographic record
Abstract
Abstract Accurate relative permeability data are essential for predicting the performance of two-phase flow through porous media. Many factors, such as the rock and fluid properties, may affect the measurement of relative permeability. However, the saturation levels of the fluids flowing through a porous medium have the largest impact on the shape of the relative permeability curves. Because relative permeability is a strong function of saturation, an accurate measurement of saturation levels in various types of two-phase flow experiments is required. In addition to many non-invasive methods, weighing and volumetric methods are frequently used to estimate the average saturation during steady-state experiments. However, for unsteady-state flow experiments, material balance methods to determine the saturation levels are relatively difficult to use. This article presents a relatively new non-invasive saturation measurement method and the equipment used to obtain dynamic saturation profiles as a function of time and distance along the core-holder. The new saturation measurement system has been found to be equally good for steady-state and unsteady-state experiments. Typical dynamic saturation profiles, the equipment calibration method, and a set of typical relative permeability curves for a co-current flow experiment are presented. Based on the presented experimental results, it has been found that the new saturation measurement method and the equipment is reliable and can reproduce stable dynamic saturation profiles with a minimum level of uncertainty. Introduction Underlying the extension of single-phase flow theory for the simultaneous flow of two or more fluids are the concepts of effective and relative permeability. The effective permeability is a relative measure of the conductance of a porous medium for one fluid phase when the medium is saturated with more than one fluid(1). The relative permeability is defined as the ratio of the effective permeability of a phase to a base permeability [e.g., absolute permeability to air or water, Craig(2)]. Relative permeability data are essential for almost all two-phase flow studies related to reservoirs. The data are used in making estimations and predictions of the productivity, injectivity, and ultimate recovery from reservoirs for evaluation and future development plans. The relative permeability data can also be used to diagnose the formation damage expected under various operational conditions. Therefore, unquestionably, these data are one of the most important data sets required in reservoir engineering studies. Among several methods for obtaining relative permeability curves, laboratory techniques are considered to be the most reliable. These methods for relative permeability measurement are further classified into steady-state and unsteady-state methods. Aleman et al.(3) have concluded that the difference in the relative permeabilities obtained by the two approaches is negligible, provided that the magnitude of the local (not macroscopic) capillary number is larger than a limiting value. Numerous studies have been conducted to investigate the effect of important parameters during the measurement of relative permeability data. In addition to saturation, some of the other important parameters affecting relative permeability are wettability, IFT, flow regime, overburden pressure and temperature. Leverett and Lewis(4), Sarem(5), Saraf and Fatt(6), and Owens and Archer(7) have shown that for strongly water-wet unconsolidated sands the permeability to a wetting phase is dependent solely upon its own saturation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".