Case Study of Hot Water Foam Flooding in Deep Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract EOR of deep waterflooded reservoir with low porosity and permeability is challenged by low heat efficiency of conventional thermal process. Conventional chemical process like surfactant flooding also has some risk because of high oil viscosity and water channeling along fractures since the wells are hydraulically fractured. The conventional waterflooding can only last for a short period of effective production and low water cut level, and the performance enhancement by EOR operations will be greatly reduced after water cut reaches to a high level. Therefore, it is quite urgent to make and apply reasonable EOR strategy for this kind of reservoirs. In this paper, the experiments were carried out to evaluate the potential of continuing conventional waterflooding and the performance of enhancing water temperature and change of injection agents, which optimized that the hot water foam flooding would be the best choice in terms of oil viscosity reduction, conformance profile control and oil displacement efficiency enhancement[1–3]. Based on the 3D geologic modelling, the waterflood target area was extracted and upscaled as the numerical simulation model to make history match and the residual oil saturation analysis. According to the current well status and well spacing, the reservoir engineering optimization was conducted to optimize the foaming system, converting timing and paramters of production and injection. Based on the reservoir engineering design, the hot water foam flooding was implemented from Janurary 2014, and the production performance is quite encouraging, which has the significant guidance for the EOR of similar deep heavy oil reservoirs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".