Semi-Analytical Solution of Chemical Flooding in Heterogeneous Non-Communicating Layers with a Focus on Low Salinity Water Flooding
Bibliographic record
Abstract
Abstract Chemical flooding has been implemented intensively for some years to enhance sweep efficiency in porous media. Low salinity water flooding (LSWF) is one such method that has become increasingly attractive. Historically, analytical solutions were developed for the flow equations for water flooding conditions, particularly for non-communicating strata. We extend these to chemical flooding, more generally, and in particular for LSWF where salinity is modeled as an active tracer and changes relative permeability. Dispersion affects the solutions, and we include this also. Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. We extend a correlation that we previously developed to predict the effects of numerical and physical dispersion. We used this correction to predict the location of the second waterfront in each layer which is induced by the chemical’s effect on mobility. We show that in multiple non-communicating layers, mass conservation can be used to deduce the interlayer relationships of the various fronts that form. This is based on similar analysis developed for water flooding although the calculations are more complex because of the development of multiple fronts. The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation. The recovery factor and vertical sweep efficiency are also very predictable. The work can also be applicable to other chemical EOR processes if they alter the fluid mobility. This includes polymer and surfactant flooding.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".