Inversing fracture parameters using early-time production data for fractured wells
Bibliographic record
Abstract
The authors studied models of inversing fracture parameters using early-time production data from fractured wells. Inverse results help evaluate the performance of fracturing, improve fracturing design, and predict the long-term production dynamics of fractured wells. First, polynomials were used to match variable flows. A new analytical model describing the transient-pressure behaviour of variable flow production was developed. This model is significantly superior to existing superposition analysis models in terms of calculation speed, accuracy, and stability. Then, in order to establish the best match between the calculated bottomhole pressure and the actual measured bottomhole pressure, the wellbore storage coefficient, fracture conductivity, fracture half-length, and fracture skin factor were selected as inverse fracture parameters. An automatic matching model was established, and a Levenberg-Marquardt algorithm based on a stochastic initial value and maximum probability was developed. This algorithm (1) is easy to implement, (2) can search local optimal solutions as much as possible, and (3) to improves the multisolution of inverse problems. Finally, the sensitivity of fracture parameters was analysed. Some existing automatic matching methods were compared and validated. A set of accurate, high-precision data acquisition and calculation devices was identified to promote application of the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".