Wellbore Integrity Evaluation Based on the Grey-Markov Model of Sand-Producing Wells in Fractured Tight Gas Reservoirs
Bibliographic record
Abstract
Abstract Some deep and abnormally high pressure fractured tight gas reservoirs in the Tarim oil and gas basin face serious sand production problems during the development stage, resulting in a decline of more than three times in gas well production, causing serious damage to the integrity of the wellbore, and bringing severe challenges to the development and management of the gas field. Therefore, it is urgent to establish a wellbore integrity evaluation model for sand-producing wells. At present, the research on wellbore integrity evaluation at home and abroad mainly focuses on establishing appropriate integrity evaluation models for different types of gas wells. It is found that there are few reports on the integrity evaluation models for sand-producing wells. However, sand production from oil and gas wells has serious erosion and plugging hazards to the formation, wellbore, gas production tree, etc. Sand production will cause wellbore integrity failure, and wellbore integrity failure will aggravate sand production. In order to accurately evaluate the wellbore integrity of sand-producing wells in fractured tight sandstone gas reservoirs, this paper predicts the integrity failure probability based on the gray Markov model, determines the weight of integrity influencing factors by using the analytic hierarchy process, and establishes a wellbore integrity evaluation model suitable for sand producing wells in fractured tight sandstone gas reservoirs. The new model can predict the integrity failure probability, and quantitatively evaluate the integrity risk of sand wells, it has important practical significance to provide the theoretical basis for the safe operation and integrity management of sand wells in such gas reservoirs. The case analysis results show that the study area will be at a low risk of integrity in the next five years, and normal production can be achieved only by normal monitoring of relevant data. However, the risk of individual wells is high, so it is necessary to strengthen attention and take necessary measures to reduce risks, which improves the prediction for improving the integrity management level of the study area.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".