Feasibility Analysis and Optimal Design of Acidizing of Coalbed Methane Wells
Bibliographic record
Abstract
Plugging is a prominent reason for production reduction in coalbed methane (CBM) wells. In order to solve this problem, authors conducted the feasibility analysis and optimal design of acidizing of CBM wells to remove the plugging in Hancheng block (H block) China. First, X-ray diffraction analysis shows that the plugging contains acid-soluble minerals and the field case indicates that acidizing effect is positively correlated with the content of acid-soluble minerals. Inspired by this, authors analyze determining factors of the content of acid-soluble minerals. Well logging parameters (DEN, AC, GR) are selected to establish a neural network model to predict the content of acid-soluble minerals. Furthermore, a feasibility criterion of acidizing of CBM wells is proposed. Then, a forward model and an inversion algorithm are proposed to diagnose the plugging. The multisolution problem of parameters inversion is solved by the Gauss–Marquardt (G-M) algorithm based on the stochastic initial value and maximum probability. Combining this method with the current numerical model of acidizing, authors present an optimal design in order to optimize the volume and injection rate of the acid. Meanwhile, by experimental study, authors propose a new acid formulation. Finally, results have been applied in the field to confirm the feasibility of the acidizing. It turns out that acidizing is an effective stimulation technology for some specific CBM wells, and the feasibility analysis and the optimal design can improve the effect of acidizing of CBM wells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".