Lessons Learned from Shell's History of Casing Conveyed Fiber Optic Deployment
Bibliographic record
Abstract
Abstract Fiber Optic Sensing is an emerging technology that can provide a means to measure a broad range of downhole events that span geophysics, completion and production engineering applications. All these measurements can be performed by installation of a single fiber optic cable; assuming reliability and life-cycle cost(s) are sufficiently robust. The multiple applications of fiber optic technology provide an effective way to maximize data collection from a single well. This data can result in a better understanding of the subsurface, which can help make more efficient and effective commercial and development decisions. For the Unconventional Gas and Light Tight Oil projects, Shell has completed a number of wells across North America with fiber permanently conveyed on the outside of the production casing and across the completion interval for the primary purpose of capturing hydraulic fracture (HF) stimulation data. Multiple fibers can be deployed in a single cable, enabling simultaneous data gathering from distinct optical technologies. As such, the installed fibers have also been used to detect micro-seismic events, capture VSP data, and to analyze stimulation flow-back and production inflow profiles. In order to fully realize the potential of fiber optic technology for the multiple applications, it is necessary that the fiber optics remain operational for a sufficient length of time to gather the desired data sets over the well life. This has proven quite challenging in this severe environment: often fiber optic cables fail prematurely during, or shortly after, deployment or during HF stimulation operations. These failures have been categorized into three general groups: downhole, surface, and supporting data collection systems. Before this technology can fully mature, it is critical that the current reliability issues are addressed. This paper will study the status of Shell fiber wells across North America, illustrate the temperature and acoustic response when a fiber fails during HF stimulation, and share the results of reliability testing on several different fiber optic cable designs.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".