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Record W2397908720 · doi:10.2118/180726-ms

SAGD Production Observations Using Fiber Optic Distributed Acoustic and Temperature Sensing: "SAGD DAS - Listening To Wells to Improve Understanding of Inflow"

2016· article· en· W2397908720 on OpenAlexaboutno aff
Warren MacPhail, J. D. Kirkpatrick, Ben Banack, Bryan Rapati, Alex Ali Asfouri

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsInflowPetroleum engineeringThermocoupleOptical fiberDistributed acoustic sensingOil wellAcousticsGeologyEnvironmental scienceFiber optic sensorEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Performance optimization of steam assisted gravity drainage (SAGD) well pairs requires awareness of unique and sometimes complex downhole processes. Reservoir monitoring tools commonly used to characterize the downhole pressure and temperature environments include thermocouples, pressure gauges, and discrete or distributed fiber-optic sensors. Distributed temperature sensing (DTS), the most common fiber-optic measurement used for SAGD reservoir monitoring, has been widely adopted for SAGD production monitoring due to its ability to accurately measure a wide variety of temperatures in harsh environments. High-measurement density along the entire SAGD well length has proven to be useful for both production optimization (Krawchick et al. 2006) and well-integrity applications. Though DTS monitoring is a primary downhole measurement tool for thermal production, other sensors may further characterize the nature of SAGD well performance when used in conjunction with DTS. Alone, temperature and pressure measurements may not yield a complete understanding of the inflow contribution in SAGD production wells. For instance, the effects of complex heat transfer may mask reservoir temperatures. Additionally, high temperatures are not always indicative of inflow and cooler liner temperatures may not signify the absence of production contribution. Distributed acoustic sensing (DAS), which is used to measure acoustic frequency and intensity in 1-m intervals along the length of a fiber-optic line, is another downhole measurement tool currently being evaluated for its ability to provide additional downhole wellbore information. Although DAS has been commonly used to characterize the acoustic environment in hydraulically fractured horizontal wells (MacPhail et al. 2012, Holley et al. 2015), it has not been extensively applied in SAGD well pairs. This paper shares select DAS and DTS monitoring data from a pilot well, the results of which improved the operator's understanding of the nature of the SAGD production. In late 2012, Devon Canada installed DTS multi-mode fiber in several production wells at SAGD assets in the McMurray Oil Sands. Single-mode fiber utilized for DAS were deployed in conjunction with multi-mode fiber, allowing simultaneous logging of DTS and DAS data throughout the wellbore. Temperature and acoustic datasets were obtained at different representative flow conditions, including stable production, rate step-down, early time shut-in, and well startup. The combined analysis of DTS, DAS, and surface production data shows that DAS was able to identify steam flashing and qualitatively define production inflow contribution and gas/liquid composition. Due to the complex, bi-directional flow in the trial well, some of these conclusions would not have established without the observations obtained from DAS monitoring.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.227
Teacher spread0.199 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations17
Published2016
Admission routes1
Has abstractyes

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