Development of Systematic Hydraulic Fracturing Technology for a Naturally Fractured Reservoir
Bibliographic record
Abstract
Abstract Reservoir N is a typical low-permeability buried-hill fractured reservoir with oil-bearing area of 3.6Km2, OOIP of 973 × 104t and buried depth of -1700 ~ -2500m.Its oil bearing interval is longer, between 125 ~ 410m.Its average porosity is 5.1% and average permeability is 23.6× 10−3µm2. Due to the insufficient natural energy, its pressure coefficient is 0.98. As the economic yield can't be achieved by conventional technologies, the reservoir must be fractured integrally. Following technologies have been applied in the field. Based on early and proper water injection, supplementing formation energy and maintaining formation pressure, fracturing and waterflooding are combined reasonably to improve waterflooding sweep efficiency and avoid watering out and water channeling at the same time; the concept of optimizing fracturing design for high sand content, large discharge capacity, medium and low proppant concentration and medium and long fractures is established so that artificial fractures can be communicated with natural fractures and a complete interconnected system can be formed by pores, artificial and natural fractures in the reservoir; with the method of perforating short intervals, avoiding perforating long intervals and staged fracturing, the whole oil-bearing interval is divided into 2 to 4 fracturing units which are fractured from bottom to up till all the interval are fractured; silt is used as the fracturing fluid loss additive to reduce excessive fracturing fluid loss and used for propping fractures and connecting major fractures and natural microfractures. Reservoir N has been fractured for 96 times by this method with cumulative oil production of 98.3 × 104t. Field applications indicate that the method of integral fracturing development is also suitable for the low-permeability fractured sandstone reservoirs which are similar to Reservoir N and good development effects have been got.
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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 | 0.000 | 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) | 0.001 | 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".