The Study and Application of Hydraulic Fracturing and Acidizing in Exploration Wells in China
Bibliographic record
Abstract
Abstract The characteristics of exploration zones in China in recent years are summarized as: 1) Special lithology such as volcano and clay-carbonate, etc. 2) low porosity and ultra-low permeability; 3) Complex storage types and fluid flow mechanics as a result of natural fractures distributed stochastically; 4) Special in-situ stress and loss characteristics; 5) complex rock mechanics and elastic-plastic traits; 6) Undetermined fracture height growth; 7) complex stress sensitivity. Therefore, a systematic technologies are put forward in the paper, the highlights are: 1) Systematic formation fine evaluation which contains Nuclear Magnetic Resonance, Constant Rate Mercury Injection and so on; 2) Low damage fracturing fluids such as super grade Guar fracturing fluid and its combination with liquid nitrogen, low cost VES(less than 300 RMB below 90 ° C), low polymer concentration fracturing fluid and linear gel, and variable viscosity is utilized in different treatment stage; 3) Different type and diameter proppants are used in one treatment job, and the combined ratio is optimized; 4) A new method of multiple stage optimization is put forward to ensure not only the success of stimulation treatment but also the maximal post-treatment performance; 5) Counter measures are applied systematically such as two combinations of pumping rate and viscosity to control loss and multiple fractures, fracture height control, systematic quality control and post-treatment management, etc. What's more, above technologies have been put in to field applications in more than 39 wells in the year of 2005, and the result is satisfied with the newly increased oil reserves being greater than 1×108 t, gas reserves greater than 800 × 108 m3. Consequently, it has a great significance in China's main exploration zones, in the mean time, it also has a great benefit of economy and hydraulic fracturing and acidizing themselves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".