The Successful Development Strategies of Tigh Oil Reservoir: Achievements versus Future Directions
Bibliographic record
Abstract
Abstract The Oligocene reservoir of White Tiger field is a tight oil reservoir with high heterogeneity and complex geological characteristics. In such complex reservoirs, a detailed geological understanding of the reservoir along with a reservoir simulation is needed to gain a detailed reservoir description and determine the optimal recovery method for this oil reservoir. These are essential in order to have successful operations as well as reducing uncertainties and improving the efficiency of oil field management. With a large database collected from initial production stages of over 50 wells, the authors developed an integrated static and dynamic workflow to forecast oil production under several production scenarios. The challenges of development strategies in such tight oil reservoirs are analyzed and compared with those one in the typical sedimentary reservoir as well as naturally fractured basement reservoirs. In addition, the authors proposed a successful assisted assisted history matching by global optimization to construct a reliable model for complex reservoirs. Finally, this paper provides a comparative evaluation of enhanced oil recovery feasibility by surfactant polymer flooding in tight oil reservoirs. A series of laboratory test and compositional reservoir simulation were performed and analyzed by both experiments and commercial simulator. The results prove that cumulative oil recovery by surfactant polymer flooding increases more than 1.44 times in comparison with water flooding. This is a solid evidence for a promising successfulness of surfactant polymer flooding in complex reservoirs.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".