Modeling of Geochemical Reactions Occurring in the Gyda Field Under Cold-Seawater Injection on the Basis of Produced-Water-Chemistry Data and Implications for Scale Management
Bibliographic record
Abstract
Summary The evidence from the produced-brine chemistry suggests that the Gyda field has experienced a variety of geochemical reactions caused by the high temperature and initial calcium (Ca) concentration, and so it is worth reviewing the produced-water data set and studying what in-situ geochemical reactions may be taking place. Produced-brine-chemistry data from 16 wells in the Gyda field are plotted and analyzed in combination with general geological information and the reservoir description. A 1D reactive-transport model is developed to identify the possible geochemical reactions occurring within the reservoir triggered by seawater injection, and then extended with the inclusion of thermal modeling and also to be a 2D vertical-cross-section model. Three possible classes of formation-water composition in different regions of the Gyda field have been identified by analysis of the produced-water data set. Anhydrite and barite precipitation are the two dominant mineral reactions taking place deep within the reservoir. Magnesium (Mg) stripping may be a result of multicomponent ion exchange (MIE), dolomite precipitation, or a combination of both. Reservoir temperature is lowered during cold-water injection. The solubility of anhydrite increases at lower temperature, and anhydrite will gradually dissolve in response to the movement of the temperature front, which is much slower than the formation/injection-water mixing front. The extent of mineral precipitation within the reservoir can be reduced by the heterogeneity; the modeling shows that the extent of ion stripping caused by mineral reactions in the reservoir is greatest when simulating a single uniform layer. Brine mixing and the occurrence of geochemical reactions caused by vertical mixing are not observable, even when assigning a high vertical permeability in a heterogeneous model. Thermal modeling is included to evaluate the effect of nonisothermal processes and heat transport on the geochemical reactions, especially the anhydrite mineral reaction. We have investigated how the difference in horizontal permeability in the two layers affects brine mixing of formation and injection water and geochemical reactions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".