A Novel Method to Detect Cement through Direct Measurement – Case Histories
Bibliographic record
Abstract
Abstract Cement quality is typically determined through the use of sonic logging tools, more commonly known as cement bond logs (CBLs), or more recently ultrasonic imaging tools (USITs). In general, these tools have served the industry well over time, but with the advent of new and exotic cement blends, as well as multistage cement jobs in today's unconventional horizontal wells, the quality and even location of the cement has become more problematic for basic CBL/USIT tools to detect. In addition, these tools are ineffective through multiple uncemented casing strings. A novel method to detect cement was developed as an offshoot of a technology used for detecting proppant in hydraulically fractured wells. This technique uses a non-radioactive tracer which exhibits a high thermal neutron capture cross section that is then incorporated into the proppant grains during manufacture. The proppant can then be detected using standard neutron-logging tools, at any time during the well's life. By incorporating small volumes of this detectible proppant into the cement slurry, the cement can then be detected using the same logging tools. This leads to identification of the top of cement, as well as the cement quality. If desired, the taggant can be staged such that the top and bottom of a cement stage can be detected. This paper will first review the industry concerns with cement detection. It will then discuss the principles and theory behind how the taggant works, both for basic proppant detection, as well as the novel application as a vehicle for cement detection. This will also include lab testing showing no impact of the tagged proppant on cement performance. The authors will conclude by presenting several case histories of cement detection, including two horizontal well applications, one each in the Permian and Canada. A third case history will also be presented in which the cement was detected through multiple strings of uncemented casing, to verify success of a cement squeeze in a surface casing remediation. This new technique allows for cement detection in wells in which conventional CBL/USITs are difficult to interpret, including detection of exotic cement blends, and through multiple strings of casing. This allows for more confidence in cement isolation, particularly in today's unconventional wells, where isolation of uphole formations is critical. This paper will be useful for drilling and completion engineers who are concerned with their ability to confirm cement quality, as well as production engineers who must perform remedial cementing operations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.006 | 0.002 |
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".