Impact of WAG Design on Calcite Scaling Risk in Coupled CO2-EOR and Storage Projects in Carbonate Reservoirs
Bibliographic record
Abstract
Abstract WAG (Water-Alternating-Gas) schemes have been applied in Brazilian carbonate reservoirs aiming to minimize residual oil saturation and gas flaring by recycling CO2 naturally being produced alongside hydrocarbon gas. However, applying WAG injection in highly reactive and heterogeneous carbonate rocks can potentially create severe scaling problems. This work develops a reactive transport simulation-based workflow to evaluate the impact of key WAG design parameters on oil recovery, scale deposition risk and CO2 storage to support multi-objective decision-making. Compositional simulations of WAG scenarios were performed as part of a sensitivity study followed by statistical analysis in order to quantify to what extent the outcomes of interest are sensitive to variations on four WAG design parameters: WAG ratio, CO2 concentration in the injection gas stream, injection rate and solvent slug-size. We established an Equation-of-State (EoS) using PVT data, a representative geochemical model and well constrains designed to control production of injected fluids. Scale risk was assessed by calcite changes around the wells, precipitation in well tubing and surface facilities, and water breakthrough. Results of this study showed that values of calcite rate constant (Ksp) and reactive surface area (A0) assigned in numerical simulations can impact relative calcite changes in the reservoir. Using reactive surface areas from BET studies of crushed rocks can lead to prediction of unrealistic amounts of calcite dissolution. Cases with lower values of (Ksp×A0) appeared to be more numerically stable and more consistent with dissolution/precipitation rates of silicate minerals. Simulation results also suggested that calcite dissolution close to injection wells and precipitation in production wells and surface facilities become more severe as CO2 concentration in injection gas and WAG ratio increases. Based on the design variables and reservoir conditions studied, the most to least crucial factors affecting oil recovery were: CO2 concentration in the injection gas stream, injection rate, WAG ratio and solvent slug-size. From a storage perspective, the impact of the design variables had considerably more impact, with the most influential factor being again CO2 concentration in the injection gas stream, followed by WAG ratio, injection rate and solvent slug-size. Optimization study results suggested that low WAG ratio values combined with low to intermediate gas slug sizes could result in superior profitability and CO2 storage outcomes for this pilot. Ultimately, we demonstrate the importance of integrating multiphase miscible displacement with geochemical reactions while modeling complex CO2-EOR in carbonate reservoirs and address how key design parameters impact our desired outcomes, knowledge that promotes a more robust decision-making framework.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".