Using Neural Network System for Casing Collapse Occurrence and Its Depth Prediction in a Middle-Eastern Carbonate Field
Bibliographic record
Abstract
Abstract A large carbonate oil field in Iran is suffering from severe casing collapses. 48 casing collapses have been found to be reservoir compaction and poro-elastic effects and corrosion. The application of neural networks for predicting casing collapses using complex multi-dimensional field data has been undertaken. This paper shows how a neural network (ANN) system can be trained based on the parameters affecting casing collapse to estimate the potential of collapse of wells to be drilled as well as the current wells producing in the field. The potential use of this type of analysis is large in that it can be linked as a critical risking parameter in future field development analysis. Being able to quantify the potential for collapse of a well in the future can give management the foundation for a better financial decision making on what wells and where to drill them with the potential for the larger net return on the investment. The estimated collapse and corresponding depth could also benefit in the type of casing design and completion method to be selected as well as workover designs. Interpretation of the neural network results, together with engineering judgment, allowed us to conclude that using this method is technically feasible for predicting casing collapses in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".