Effects of permeability, grid resolution, and carbonate reactions on CO2 distribution and sulphate scaling in seawater-flooded carbonates
Bibliographic record
Abstract
Abstract During oilfield production, mineral scale deposition in production wells and surface facilities presents major difficulties, particularly when water breakthrough occurs. Hard sulphate scales, like barite, are frequently the result of incompatible interactions between the formation and injection waters. In contrast, carbonate scales are caused by variations in the temperature, pressure, pH, composition of the brine, and the amounts of CO2 in the aqueous and hydrocarbon phases. In waterflooded reservoirs where injection water composition can be controlled, this study investigates the effects of temperature, ionic concentration, pH, and CO2 availability on the risks of carbonate and sulphate scaling. Particularly, scaling hazards in carbonate-rich formations are greatly influenced by precipitation of magnesium-rich carbonate. The significance that CO2 partitioning from hydrocarbons into injected saltwater plays in scaling estimates has been ignored in earlier studies. To close this gap, this study investigates how temperature and reservoir pressure affect oil recovery and scale management, particularly in systems that dip below bubble point pressure. A commercial reservoir simulator, which couples aqueous and mineral geochemistry with three-phase fluid flow calculations, has been used in this study. Equilibrium reactions have been considered in three-dimensional (3D) models. Oilfield data have been used to identify the parameters of significance to consider in the calculations, such as ionic concentrations, hydrocarbon composition, mineral components etc. Key findings, importantly, the results identify that for the reservoir temperature of 100 °C considered and for the primary mineral assemblage, calcite dissolution and magnesium-rich carbonate precipitation are interdependent. They are affected by the abundance of CO2 in the residual oil phase, and this evolves over time, impacting the concentration of calcium and magnesium in the brines traversing the reservoir. Temperature changes around the injection wellbore also impact component and mineral solubilities, especially in terms of anhydrite and gypsum reactions. All these factors impact the calcium, magnesium, barium, strontium, sulphate and bicarbonate concentrations at the production well, and hence the scaling risk in the production system. In conclusion, managing reservoir conditions such as temperature, ionic concentrations, and CO2 distribution is important for reducing the likelihood of scale deposition while preserving oil recovery in carbonate-rich, water-flooded 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".