Heated Area and Well Performance Analysis of Injection N 2 and CO 2 in Cycle Steam Stimulation Process
Bibliographic record
Abstract
Application of steam injection technology to heavy oil reservoirs is the most commercially successful EOR method. Cycle steam stimulation (CSS) is known as the most widely used and mature technology compared with various thermal methods. Because of various reasons, such as too high initial oil viscosity, excessive overburden heat loss and so on, in CSS, the radius of heated zone is small and the viscosity of heavy oil still cannot be lowered effectively, which leads to the low oil productivity and poor oil well performance. A variation on CSS process is to add N 2 and CO 2 in steam injection. Because of the influence of the N 2 and CO 2 , the heated area and well performance of N 2 and CO 2 assisted CSS are different from that of steam stimulation. Therefore, this paper describes a detailed study of N 2 and CO 2 influence to cycle steam stimulation. In this paper, the physical simulation experiments of N 2 and CO 2 influence to the mixture of heavy oil are carried out at first. Through physical experiments, the enhancing oil mechanisms of N 2 and CO 2 ,the recovery mechanism of reducing oil viscosity by CO 2 dissolving, reducing interfacial tension between gas and heavy oil, which are different from the steam, are described respectively. Based on this, a numerical simulation model with a single horizontal well is built to carry out the quantitative and comparative study of heated area of formation. Results show that the development effect of N 2 and CO 2 assisted CSS is better than that of conventional steam stimulation in porous media. Next, the different well performance of the N 2 and CO 2 assisted CSS and conventional CSS are compared by numerical results. Finally, on the basis of the field data of two different heavy oil field, two typical wells of CSS and N 2 and CO 2 assisted CSS are analyzed in detail. Consequently, the N 2 and CO 2 injection together with steam is helpful to improve development effect in CSS process.
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 | 0.001 |
| 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".