In Situ Carbon Dioxide Generation for Improved Recovery: Part II. Concentrated Urea Solutions
Bibliographic record
Abstract
Abstract While injection of CO2 has great potential for increasing oil production, this potential is limited by site conditions and operational constraints such as lack of proper infrastructure, limited cheap CO2 sources, viscous fingering, gravity override at the targeted zones, and so forth. To mitigate some of these common limitations, we explore alternative methodologies which can successfully deliver CO2 through gas generation in situ, with superior IOR performance, while offering reasonable chemical cost. While dissolved easily in reservoir brine, urea is thermally hydrolyzed to CO2 and NH3 after equilibration under reservoir conditions. Therefore, given its exceptional compatibility with reservoir fluids, its CO2 producing capacity and reasonable cost benefit, urea appears to be a promising candidate for delivering CO2 to increase oil recovery. The in-situ gas generation requires single chemical slug, which can minimize the complexity of the injection system. One-dimensional sand pack tests and core flooding experiments were operated at pre-set conditions: different API gravity oils were used, varying from 27 to 57.3. In addition, the reaction rates of the urea hydrolysis and urea solution PVT property were tested separately under reservoir conditions. Most importantly, results of injecting urea solution (as low as 10 % solution) showed superior tertiary recovery performance (as high as 37.97%) are realized as compared to the most recent efforts at our group (29.5%) as well as similar in situ CO2 generation EOR (2.4% to 18.8%) approaches proposed by others. The economic feasibility and operational advantages of this newly developed method were demonstrated in this work. In brief, results of this work served further as a proof of concept for designing in situ CO2 generation formulations for tertiary oil recovery at both onshore and offshore fields under proper conditions.
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 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 | 0.000 |
| 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 | 0.001 |
| 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".