Lessons Learned and Experiences Gained in Developing Waterflooding Concept of Granite Basement Fractured Reservoir: 20 Historical Years Case Study
Bibliographic record
Abstract
Abstract NFR represent over 20% of the world's oil and gas reserves. However, characterization of fractured reservoirs is complex and presents unique challenges in comparison with conventional reservoirs. It is immensely difficult to achieve the best results in secondary recovery process. This paper presented the successful developments of waterflooding to overcome the complex geological characterizations of White Tiger field - the biggest fractured basement reservoir up to now on the continental shelf of Viet Nam. This reservoir has a complicated geological structure, very high heterogeneity, high temperature and closure stress. The total OIIP of this field reached nearly 4 billion barrels with 2000 meters of the oil bearing thickness and has been produced by more than 100 wells, tens of which flow at the rate of approximately one thousand barrels per day. The geological study and fractured model were carefully investigated in both micro and macro scales to improve waterflooding performance. The authors analyzed the advantages and disadvantages of injection systems in basement reservoir during 20 historical years. And then, the artificial water buffer solution was proposed to develop waterflooding process. The authors described the establishment and the association of local artificial water buffer in basement reservoir. An effective method to adjust the injected water mass was also discussed. The excellent results in White Tiger field proved that average reservoir pressure and total oil recovery were significantly increased in comparison with previous injected schemes. This paper is the precious guideline to solve some typical problems of waterflooding in fractured basement reservoir such as: What could be applied waterflooding in basement fractured reservoir? What is the optimum injected rate and injected mass for basement fractured reservoir? How to evaluate the probability of high water cut in production wells during waterflooding process? How to predict the rising of artificial water-oil contact?
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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) | 0.000 | 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".