Characterization of Flow Control Device Performance with Distributed Fiber-Optic Sensors
Bibliographic record
Abstract
Abstract It is becoming common to install inflow control devices (ICDs) along steam-assisted gravity drainage (SAGD) production liners to enhance temperature conformance and accelerate depletion. Additionally, some operators advocate the installation of similar outflow control devices (OCDs) along the injection well of the SAGD well pair. Collectively, these inflow and outflow devices are often referred to as FCDs. Industry adoption of flow control devices (FCDs) has increased, and several devices are commercially available for use in SAGD. In an effort to optimize FCD design and selection, a joint industry partnership (JIP) was formed (Vachon et al. 2015) and flow loop testing conducted to establish FCD performance curves and erosion tolerance over a wide range of pressure, temperature, and steam quality (SQ) consistent with a typical SAGD well environment. In conjunction with flow loop testing, several full-scale FCD deployments were completed at the JIP fields, including pilot wells at the production company's SAGD facility, which were logged with fiber-optic technology. Fiber-optic-based instrumentation was deployed within FCD-equipped wells using permanently installed coiled tubing. Well architecture design changes to a typical completion were not required because fiber-optic sensors are used for most non-FCD wells to collect distributed temperature sensing (DTS) data. Although DTS is a common tool for optimizing SAGD production, it has certain limitations; specifically, temperature changes along production wells do not typically allow a detailed definition or quantification of the inflow distribution along the wellbore. In addition to DTS, distributed acoustic sensing (DAS) was periodically performed on the FCD wells. DAS logging of SAGD producers has several potential uses, including flow profiling, steam breakthrough and/or noncondensable gas (NCG) detection, multiphase flow characterization, electric submersible pump (ESP) performance, completion failure analysis, and four-dimensional seismic analysis. Although FCD characterization with DAS appears promising, a knowledge gap exists as to how to move beyond qualitative analysis to more quantitative analysis of FCD performance and the lateral emulsion inflow distribution. Pending satisfactory results, DAS logging on active wells can potentially be completed to accelerate improvements of SAGD FCD performance and design as well as increase the efficiency of SAGD recovery through improved steam/oil ratio (SOR) and an associated reduction in greenhouse gases. This paper describes piloting the collection and analysis of DTS and DAS data to help improve understanding of SAGD inflow distribution. Logs were performed on multiple wells during stable and transient flowing conditions. Early surveillance demonstrated suitability and limitations of fiber-optic-based logging to validate FCD performance in active wells. In addition to field logging, acoustic recording using JIP flow loop testing was completed with accelerometers, geophones, and fiber-optic cables during FCD characterization. The goal was to cross reference the acquired acoustic signals for quantification of flow at devices and validation of performance. An overview of the JIP flow loop FCD acoustic characterization program is described.
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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) | 0.001 | 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".