Reactive transport modeling of scale precipitation and deposition during incompatible water injection in carbonate reservoirs
Bibliographic record
Abstract
Abstract Seawater injection is an efficient enhanced oil recovery (EOR) method that capitalizes on the chemical composition differences between the injecting seawater and in-situ formation water, which leads to physicochemical interactions between the rock and fluids. These rock and fluid interactions result in changes of rock wettability and subsequent improved microscopic sweep efficiency. However, the ion imbalance resulting from seawater injection and its incompatibility with the in-situ formation water may interfere with the rock and fluids equilibrium state, causing scale precipitation and subsequent deposition which can negatively impact rock quality, well productivity and reservoir performance. In this study, an accurate, robust, and general approach is presented by coupling a geochemical module with a compositional two-phase fluid flow model to handle reactive transport in porous media. The proposed coupled model, so-called ad-scale model, is capable of simulating carbonate rock dissolution and sulfate scale formation/deposition for evaluating reservoir performance under incompatible water injection. The model predictions were validated using experimental data. This model was also utilized to predict water injection rate into a carbonate formation. It was obtained that both the reacting and non-reacting component profiles were accurately predicted using the proposed coupled model. The water injection rate prediction was also validated and showed high accuracy with absolute error and coefficient of determination values of 9.02% and 0.99, respectively. In addition, a sensitivity analysis was performed on water composition, which showed a strong dependence of reservoir and well performance on water composition. Graphical abstract This diagram elucidates what exactly happens during incompatible water injection in the mixing zones near the injection well (right half of the figure) or production well (left half of the figure) where most of the geochemical phenomena occur.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".