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Record W4404894847 · doi:10.2118/223127-ms

Permanent DAS/DTS Monitoring for SAGD Production – Design, Results, and Recommendations

2024· article· en· W4404894847 on OpenAlexaffabout
Praise Luckanachai, Andrea De Martino, Kevin Boone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsHaliburton Forest & Wild Life Reserve
Fundersnot available
KeywordsProduction (economics)Petroleum engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Designs and preliminary results from a multi-year project for simultaneous Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) executed across multiple wells on a Steam-Assisted Gravity Drainage (SAGD) field in Canada is presented. Primary objectives of the project were to demonstrate prototypical execution of DAS and DTS data collection on a standard SAGD pad setup and to characterize inflow across the production zone. Challenges and learnings from this technically demanding project are explored alongside a discussion on the improving viability of continuous acoustic monitoring to augment temperature sensing commonly utilized in SAGD instrumentation design. The DTS technique employs a combination of an optoelectronics topside instrument and a downhole sensing fiber to produce a distributed temperature profile of the fiber string. The topside instrument comprises a laser light source, a fast optical detector and a combination of electrical and optical circuits to control light pulsing, and the detection and measurements of Raman backscattered light containing encoded temperature information about the temperature profile. The sensing fiber type is selected based on suitability with the topside instrument and the expected maximum operating temperature of its intended sensing environment. While DTS is compatible with both single-mode (SM) and multi-mode (MM) fiber type, MM graded-index fibers are often selected for SAGD environment due to its larger optical core size. Using a MM fiber allows for increased capture of the backscatter Raman light, a much weaker light signal than the laser light launched into the optical fiber. Temperature dependent Raman signals are extracted from the returned light and by applying the Optical Time Domain Reflectometry (OTDR) technique, which produces a temperature trace consisting of temperature magnitude matched to each unit distance along the sensing fiber. The temperature trace can vary in its spatial resolution subject to the topside instrument configuration with typical resolving resolution between 0.5 – 1.5 meters. Other general DTS sensing techniques are described in the literature (for example, Hartog (2017)) but are not described here. Some of the earliest uses of DTS in thermal enhanced oil recovery (EOR) was to monitor steam breakthrough and pump vapor locking in California's steam flood as reported by Carnahan (1999). Since then, advancement in specialty fiber optics suitable for thermal environment upwards to 300°C has enabled mainstream adoption within SAGD. Uses of DTS data has expanded to include, but not limited to, monitoring of well and steam chamber conformance, tubing and casing integrity, gas ingression and water coning. DAS is an optical interferometry technique employing an optoelectronics topside instrument and a downhole sensing fiber to detect minute changes in fiber length at each location along the fiber string. The instrument is sensitive at the pico-strain (ρε) level with a spatial resolution equal to a configurable distance referred to as the gauge length. The term "acoustic" in Distributed Acoustic Sensing broadly designates any mechanical perturbation able to alter the local state of strain anywhere along the optical fiber, be it from dynamic pressure changes, or dynamic strain (e.g. vibration). While DAS is compatible with both SM and MM fiber type, deployment alongside SM fiber types have been traditionally preferred as the laser light coherence is better preserved throughout its round-trip propagation compared to MM fiber. However, DAS with MM fiber can still be used for typical fiber lengths in SAGD wells without significant penalty in signal quality as shown in literatures (MacPhail (2016), Burke (2022). This cross-compatibility with MM fiber allows collection of DAS on existing DTS fibers deployments without further intervention. Prior to DAS adoption in oil and gas, it was predominantly deployed in defense and security application to perform parameter monitoring and ground disturbance detection. As DAS technology evolved, it was first trialed in Shell's unconventional tight gas wells in February 2009 to monitor plug setting, perforation shots and active clusters during fracking operation (Molenaar et al. 2012). In EOR application like SAGD, DAS provides an augmenting dataset correlating to production inflow phases (MacPhail et al. 2016) which would not be readily detectable in the temperature domain alone. Efforts like the one presented in this paper are underway to progress horizontal inflow profiling and inferred relative volumes which aids in understanding well productivity and efficacy of added inflow control devices (ICD). Unlike DTS, acoustic data is largely unaffected by temperature masking due to well completions. For events where insufficient temperature deviation are produced, DAS may be used to surveille for common industry challenges such as steam coning, gas ingression, loss of tubing/casing integrity and more. The emerging market segment for Carbon Capture and Underground Storage (CCUS) is poised to utilize DAS as one of the many leak detection toolkit for the injection monitoring wells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.022
GPT teacher head0.270
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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