The Quantitative Description of Tight Sand Reservoir Fracture in Sulige Gas Field
Bibliographic record
Abstract
The Sulige gas field is a typical tight sand reservoir, Ordos Basin, the fracture development degree directly affects the development of the natural gas, especially affects the volume fracturing effect of horizontal well and the deliverability of the natural gas, the appropriate method to describe the fracture is very important. At present, outcrop, core and FIM are general methods to describe the fracture development degree, but they have inherent shortages. In the Sulige gas field, a few of cores and poor representativeness can not illustrate the fracture distribution in well bore, and imaging logging data are especially absent. Su53 gas field was used as an example in the Sulige gas field, during the research of the fracture, based on fractal theory, through using the amplitude difference data between deep investigate double lateral resistivity (Rd) and shallow investigate double lateral resistivity (Rs), and other conventional logging data, in combination with the response characteristics of fracture in the logging curve, reservoir fracture was quantitatively identified by the analysis method of R / S , the relationship between fractal dimension value and fracture density was quantitatively analyzed , the vertical heterogeneity of fracture distribution was analyzed, and the relationship between fractal dimension value of fracture and initial deliverability of gas well was researched. The results which were tested by the data of drilling cores and productions available, indicate that the method of R / S is feasible to quantitatively describe the fracture development degree; the relationship between fractal dimension value and fracture density is positively relative, fractal dimension value is bigger, the fracture is more developed; there is a good corresponding relationship between fractal dimension value and deliverability of gas well, fractal dimension value decreases with a decrease of deliverability. Key words: Fractal; R / S analysis; Logging curve; Tight sand reservoir fracture; Fracture description
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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".