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Record W1982829500 · doi:10.2118/00-01-tb

Real-time Monitoring of SAGD Wells

2000· article· en· W1982829500 on OpenAlexaboutno aff
Robert Knoll, K.C. Yeung, Mehmet Saltuklaroglu

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringInjectorDrillingCasingWater wellSteam injectionEngineeringOil wellEnvironmental scienceMechanical engineeringGeotechnical engineeringGroundwater

Abstract

fetched live from OpenAlex

Technology Brief The value of continuous monitoring in SAGD (steam assisted gravity drainage) wells has been documented in various technical literature over the last two years. Of the 39 SAGD well pairs that have been drilled in Alberta to date, many have incorporated pressure and temperature monitoring as part of the assessment of SAGD effectiveness(1). Real-time monitoring has provided operators increased understanding as to the overall development of the steam chamber and the operational balance that must be achieved between injector and producer wells. Monitoring systems have consisted of thermocouples, bubble-tubes, high temperature pressure sensors, and fiber optic DTS (distributed temperature systems). Various conveyance technologies have been used to instrument injector, producer, and observation wells including casing, tubing, suspended, and coil-tubing systems. The latter is emerging as the system of choice where operators want complete understanding of horizontal pressure and temperature profiles from heel to toe, primarily in producer wells. As indicated by several early adopters of SAGD technology real-time monitoring has provided a number of benefits, which can be summarized as follows:Real-time pressure and temperature is used to obtain operational data for the design and drilling of horizontal wells and for containment and control of SAGD producers(2).Horizontal well length can be optimized based on a better understanding as to the in situ pressure drop that occurs from heel to toe of SAGD wells(1).Monitoring of multipoint pressure and temperature confirms the existence of thief zones, which can adversely affect the balance between injected steam and produced oil.FIGURE 1: Coil tubing conveyed SAGD well monitoring system (Available in full paper)Heel pressure monitoring is used to understand pumping system efficiencies and to confirm pressure and temperature distribution from the toe to heel of the well.SAGD reservoirs tend to be somewhat unconsolidated in nature and thus injection pressures must be monitored (in-situ) to remain under the known frac gradient in the area. Exceeding frac pressure can cause the loss of steam to adjacent thief zones, adversely affecting well production. As SAGD technology evolves from the pilot to commercial stages of development, there exists several viewpoints as to the amount and type of instrumentation necessary to successfully develop and operate a SAGD project. It is certain, however that continuous monitoring plays a key role in successfully producing SAGD wells. FIGURE 2: Tubing conveyed SAGD well monitoring system (Available in full paper) FIGURE 3: Suspended (observation well) monitoring system (Available in full paper) FIGURE 4: Thermal profile (multipoint measurement along horizontal well) (Available in full paper) PROMORE Engineering Inc. is an industry leader in the design, deployment and service of SAGD well monitoring systems incorporating pressure and temperature sensors, fiber optics, thermocouple and bubble-tube technologies. COMING NEXT MONTH...Beating the Odds: Enhanced Oil Recovery . Production companies are continually seeking methods to increase in oil recovery while lowering their costs. Whether you are squeezing additional production from mature fields or finding ways to economically exploit undeveloped resources, these topics will be covered in depth in the February issue of the JCPT.

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 score0.703
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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