Fibre Optic Sensing For Improved Wellbore Production Surveillance
Bibliographic record
Abstract
Abstract Since our previous publication1 significant progress has been made to further mature the application of Fiber-Optic (FO) based Distributed Acoustic Sensing (DAS) for production and injection profiling. A considerable number of new field surveys were conducted to further improve the evaluation algorithms or workflows which convert the DAS noise recordings into flowrates from individual zones. For gas producing wells, a new graphical user-interface has been developed that allows the user to visualize and QC the data in real time. Additional flow and visualization software have been developed for single phase gas producers to enable the user to select and evaluate the data in a user-friendly manner using the most up-to-date evaluation algorithms. There are still improvements to be made in enabling Distributed Sensing infrastructure, such as handling and evaluation of very large data volumes, seamless FO data transfer, the robustness & cost of the FO system installation, and the overall integration of FO surveillance into traditional workflows. It will take some time before all these issues are addressed but we believe that FO based applications will play a key role in future well and reservoir surveillance. In this paper we present two recent examples of single-phase flow profiling using DAS. The first example is from a single-phase gas producer in one of the Unconventional plays in North America and the second example is from a long horizontal, smart polymer injector operated by Petroleum Development Oman (PDO). Introduction In oil and gas field development there is often a lack of high quality Well and Reservoir Surveillance (WRS) data for quality decision making; leaving significant reservoir or well performance uncertainties potentially leading to suboptimal reservoir development. The need for frequent and good quality surveillance data is highest in complex reservoir developments such as Unconventional plays, waterflooded reservoirs, Thermal and Chemical Enhanced Oil Recovery (EOR) projects. One of the reasons that well surveillance data is not acquired in practice is that it often causes significant production deferment. Another reason is that often the data gathering surveys are expensive or create large operational risks associated when using conventional logging methods, particularly in high rate, highly deviated or long horizontal producer wells. In some cases, the small diameter production tubing limits access to the well with conventional logging tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".