Distributed Fiber-Optic Sensors Characterize Flow-Control-Device Performance
Bibliographic record
Abstract
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195869, “Characterization of Flow-Control-Device Performance With Distributed Fiber-Optic Sensors,” by Ben Banack, SPE, Halliburton; Lyle H. Burke, SPE, Canadian Natural Resources; and Daniel Booy, SPE, C-FER Technologies, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. The complete paper describes piloting the collection and analysis of distributed temperature and acoustic sensing (DTS and DAS, respectively) data to characterize flow-control-device (FCD) performance and help improve understanding of steam-assisted gravity drainage (SAGD) inflow distribution. Fiber-optic-based instrumentation was deployed within FCD-equipped active wells using permanently installed coiled tubing. Logs were performed on multiple wells during stable and transient flowing conditions. Additionally, acoustic recording using 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 flow-loop FCD acoustic characterization program is described. Introduction Installation of inflow control devices (ICDs) along SAGD production liners is common 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. Several FCD devices are commercially available for use in SAGD. Methodology In an effort to optimize FCD design and selection, a joint industry partnership (JIP) was formed and flow-loop testing conducted to establish FCD performance curves and erosion tolerance over wide pressure, temperature, and steam-quality ranges 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. These wells 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 DTS data. Although DTS is a common tool for optimizing SAGD production, it has certain limitations. Specifically, temperature changes along production wells typically do not allow a detailed definition or quantification of the inflow distribution along the wellbore. In addition to DTS, DAS was performed periodically on the FCD wells. DAS logging of SAGD producers has several potential uses, including flow profiling, steam breakthrough or noncondensable gas (NCG) detection, multiphase flow characterization, electric submersible pump (ESP) performance, completion failure analysis, and 4D seismic analysis. Although FCD characterization with DAS appears promising, a knowledge gap exists regarding how to move beyond qualitative analysis to quantitative analysis of FCD performance and the lateral emulsion inflow distribution. Pending satisfactory results, DAS logging on active wells potentially can 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 and an associated reduction in greenhouse gases.
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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 | 0.000 |
| 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 | 0.001 |
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".