Improved Logging Techniques and Interpretation Experiences to Evaluate Perforate-Wash-Cement Intervals
Bibliographic record
Abstract
Abstract At times during the lifetime of an oilfield well, there is a need to establish or rectify a barrier by using the perforation-wash-cement (P/W/C) remedial procedure. Evaluation of the P/W/C interval is carried out by sonic and ultrasonic logging tools. Depending on perforation hole size in the casing the bond log quality can be affected, rendering the logging results invalid or qualitative only. Knowledge gathered in the Norwegian Continental Shelf (NCS) has allowed for improvement in techniques and data processing workflow for the P/W/C procedure. The toolstring is reconfigured to acquire data at the highest resolution possible across an optimized logging interval. Originally, the procedure was standard cement evaluation, but through experience, the logging and data processing methodology has been improved to better accommodate the challenges posed by the P/W/C environment. Acoustic impedance images are processed with an advanced methodology widely used for open hole geological images. It applies linear feature reconstruction (inpainting) and a texture reconstruction algorithm in a multipoint statistical approach to predict data behavior in the perforation-affected interval. Different processing options are available, allowing comparison to select the most representative result. Behind the casing, cleanup efficiency depends on many parameters, including perforation design. The log data quality is greatly influenced by the density of the perforation shots, the entrance hole size, and perforation pattern. To evaluate the cement, a centralized toolstring, speed-corrected depth, and high-resolution datasets contribute to obtaining a highly detailed cement map of the annular conditions across the P/W/C intervals. This allows the implementation of further processing to strengthen the possibility of accurately reconstructing the acoustic impedance maps to provide the operator with an informed evaluation of the cement status. In multiple cases, the bond quality of the annular contents prior to the P/W/C operation have been logged to establish a baseline and make a proper pre- and post-evaluation, and such logging is recommended. In addition, blank sections due to gun connections across the perforated intervals allow an additional check to the overall log quality. Lessons learned from P/W/C logging were shared in the NCS across multiple operators and globally.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".