MétaCan
Menu
Back to cohort
Record W3129201599 · doi:10.2118/150497-ms

Using a New Intelligent Completion Strategy to Increase Thermal EOR Recoveries–SAGD Field Trial

2011· article· en· W3129201599 on OpenAlex
Joel Shaw, Mark Bedry

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSPE Heavy Oil Conference and Exhibition · 2011
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorSteam-assisted gravity drainageCompletion (oil and gas wells)Enhanced oil recoveryPetroleum engineeringSteam injectionOil fieldEngineeringProcess engineeringInjection wellEnvironmental scienceWaste managementOil sandsMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract A completions strategy has been developed for improving both steam injection and production conformance in a thermal- enhanced oil recovery (EOR) project by using intelligent completion technology that incorporates interval control valves (ICVs), well segmentation, and instrumentation. The initial field trial is ongoing in the injector of a Northern Alberta steam-assisted gravity drainage (SAGD) well pair. Depending on the level of heterogeneity present in the reservoir, the application modeling shows that a 45% reduction in the steam-oil ratio and an almost 70% increase in recovery can be achieved in a SAGD process when both improved injection conformance and producer differential steam-trap control can be applied in a segmented horizontal well pair. A cost-effective intelligent completion solution to achieve this segmentation and control has the potential to add substantial value to field developments resulting in increased energy efficiency and oil recovery through improved steam conformance. The method being developed is also applicable to a wide range of other thermal EOR processes such as cyclic steam stimulation (CSS), steam drive and variations, which include those processes involving solvent additives. The initial field deployment in the injector well was conducted primarily to prove the technology, to demonstrate the feasibility of modifying the steam distribution, and to determine best practices for future developments. A successful installation and commissioning of the intelligent completion has validated the technology substantially. Lessons learned are highlighted. Early injection test results and data show a significant increase in the understanding of the injection and production behavior in the well pair. The intelligent completion technology under trial and proposed developments should enable more extensive use of downhole measurement and control in thermal EOR projects than has been possible to date. This paper discusses the development of the completion technology, its applicability to thermal conditions, initial field trial results and the plans for further development. A test program to optimize the distribution of the steam injection in the well is underway, and the results to date also will be discussed.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.140
GPT teacher head0.313
Teacher spread0.173 · 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