Multiphase flow model of CO2 and formation fluid for production wellbores in CO2 geological storage
Bibliographic record
Abstract
Accurate prediction of multiphase flow behavior in production wellbores is critical for understanding CO 2 migration and fluid dynamics during geological carbon storage. However, existing wellbore multiphase flow models have limited capability in accurately describing the coupled hydrodynamic and thermodynamic behavior of gas–liquid mixtures under high-CO 2 and high-pressure conditions, due to the strong non-ideal thermophysical and phase-behavior characteristics of CO 2 –formation fluid systems. This study develops an improved multiphase flow model that couples a modified flow-pattern identification approach with an interphase mass-transfer model based on flash-calculation theory. A novel method is proposed for identifying multiphase flow patterns in CO 2 flooding production wells. Additionally, leveraging the fluid phase characteristics of an oil field that is undergoing CO 2 flooding for a period of time, an interphase mass transfer model for the wellbore is developed. By integrating this model with the newly proposed flow pattern identification method, an improved predictive model for wellbore pressure drop is established. The model’s reliability is validated using actual wellbore pressure data. For example, when applied to a CO 2 flooding production well in an oilfield, the predicted pressure values closely match the measured data, with a relative average error of −1.9% and an absolute average error of 7.15%, demonstrating high accuracy. The developed model provides a robust theoretical and technical foundation for optimizing CO 2 injection and production strategies in geological storage and EOR operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".