Lessons Learned and Experiences Gained in Developing the Waterflooding Concept of a Fractured Basement-Granite Reservoir: A 20-Year Case Study
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Bibliographic record
Abstract
Summary Naturally fractured reservoirs (NFRs) represent more than 20% of the world's oil and gas reserves. However, their characterization is complex and presents unique challenges in comparison with conventional reservoirs. It is immensely difficult to achieve the best results in the secondary-recovery process for NFRs. This paper presents a successful development of waterflooding to overcome the complex geological characterization of the White Tiger field, the largest fractured basement reservoir to date on the continental shelf of Vietnam. This reservoir has a complicated geological structure, with high heterogeneity, high temperature, and high closure stress. The total oil initially in place (OIIP) of this field reached nearly 4 billion bbl from 2000 m of oil-bearing thickness, and the field has been produced by more than 100 wells, 10 of which have flowed at the rate of approximately 1,000 B/D. The geological study and fractured model have been carefully investigated in both micro- and macroscale to improve waterflooding performance. The authors have analyzed the advantages and disadvantages of injection systems in this basement reservoir during 20 years of production history, and an artificial water buffer solution has been proposed to improve the waterflooding process. The authors have described the establishment and association of local artificial water buffer in the basement reservoir. An effective method to optimize the injected-water volume has also been discussed. Promising results from the White Tiger field have shown that the average reservoir pressure and total oil recovery have increased significantly in comparison with previous injection schemes. This paper presents useful guidelines to solve some typical problems of waterflooding in fractured basement reservoirs: What can be applied in waterflooding for a fractured basement reservoir? What is the optimal injection rate and injected volume for the fractured basement reservoir? How do we evaluate the probability of high water cut in production wells during the waterflooding process? How do we predict the rise of an artificial water/oil contact (AWOC)?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 it