Considering the CO2 Source and Capture Technique to Reduce Minimum Miscibility Pressure (MMP) for Enriched Water Alternating Gas (WAG) Injection
Bibliographic record
Abstract
Abstract CO2 is a well-known and commonly used solvent for enhanced oil recovery (EOR). CO2-rich natural gas fields have been the source of CO2 for onshore EOR for more than 50 years. Offshore, the story is different. Some jurisdictions like offshore Norway and Gulf of Mexico send their gas to market, pipelines from offshore to Europe and the United States exist, and CO2 must be stripped from the natural gas to meet pipeline specifications. Oil rig power generation has, for the most part, been electrified. Other jurisdictions like offshore Newfoundland, Canada, have stranded uneconomic, sweet natural gas. Power generation relies on diesel or natural gas combustion turbines producing post combustion CO2. Onshore, CO2 floods are common where most of the CO2 comes from natural gas sweetening. Post combustion CO2 has been used for EOR in Weyburn, Saskatchewan, Canada and other onshore fields. The Hibernia EOR Research Group has been investigating the integrated capture and injection of CO2 from post combustion for the purposes of EOR. Challenges include the space and size of CO2 capture technologies for offshore oil production platforms, most certainly existing brownfield facilities. From an EOR perspective, a notable challenge is the constrained volume of CO2, which is insufficient for CO2 flooding. The CO2 volumes are however sufficient for carbonated water injection (CWI), individual block CO2 flood or WAG, or CO2 enriched natural gas WAG. Current carbon capture technologies are not 100% efficient, resulting in impurities in the CO2 stream, such as N2, CH4, O2, etc. The CO2 and impurity concentrations impact the minimum miscibility pressure (MMP) and subsequent oil recovery. The relationship between different CO2 capture technologies and the resulting impurities, their respective concentrations, and the impact on MMP is deficient in the literature. Experimental techniques to estimate MMP were compared based on the literature, and it was determined that a slim tube test is the most reliable method. In this work, the CO2 concentration is varied from 0 to 100 mol%, which covers the missing range in literature. The Multiple Contact Miscibility (MCM) was first simulated, providing a good estimation of the MMP value. A slim tube simulation was completed using PVT-sim and validated with experimental values from literature. This simulation was then used to determine MMP when CO2 concentration is varied. The results indicate that MMP is reduced by increasing the concentration of CO2 in the natural gas. The amount of CO2 required in Gas Mixture to achieve MMP were deduced for each scenario. Furthermore, impurities can positively or negatively impact the MMP, even in small concentrations. This work investigates, by simulation, the effect on MMP of CO2 and natural gas mixtures, and impurities in the CO2 stream based on source and capture techniques. The study is critical to the design of an integrated CO2 capture and injection process to store CO2, reduce emissions, and enhance oil recovery.
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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) | 0.001 | 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 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".