High-Accuracy Deformation Identification in Complex Completions Using High-Resolution Acoustic Imaging
Bibliographic record
Abstract
Abstract High-resolution acoustic imaging is being deployed globally to assess well integrity in a variety of complex completions. The two inspections in this paper represent programmatic use of this technology to investigate well integrity threats, focusing on areas where deformation assessments were previously more complex. This solid-state acoustic imaging technology inspects cased wells by utilizing up to 512 independent sensors, which capture amplitude and time of flight measurements in the form of 3D point cloud data. This data is then visualized and processed into 3D renderings, conventional 2D logs, intensity images of pipe surface texture, and sub-millimetric measurements of inner and outer diameter wall loss. These visualizations have enabled novel assessment of corrosion and pitting, breaches, casing deformation,and perforated/engineered punches. This novel technology also enables assessment of threaded connections and provides measurement of over-torqued and deformed connections. The sensor array is controlled entirely electronically via software to focus the acoustic energy at the casing wall, and this focal distance is adjusted on the fly. This electronic focusing enables multiple casings or tubing strings of varying diameter ranges to be assessed in a single run. Defects and deformations are identified using proprietary surface detection and defect localization algorithms, all resulting in more efficient and valuable cased hole integrity inspections. This paper introduces this technology, validates core well integrity applications, and highlights specific findings from twowells logged in 2023 and 2024. Several unique findings were observed and the novel images from some of the most actionable findings are presented in this paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".