MétaCan
Menu
Back to cohort
Record W2280471793 · doi:10.2118/170101-pa

Characterizing the Effects of Lean Zones and Shale Distribution in Steam-Assisted-Gravity-Drainage Recovery Performance

2015· article· en· W2280471793 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsSteam-assisted gravity drainagePetroleum engineeringOil shaleInjectorSaturation (graph theory)Permeability (electromagnetism)Ranking (information retrieval)GeologyDrainageEnvironmental scienceAsphaltEngineeringOil sandsComputer scienceMathematicsMechanical engineeringMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Summary Performance of steam-assisted gravity drainage (SAGD) is influenced significantly by the distributions of lean zones and shale barriers, which tend to impede the vertical growth and lateral spread of a steam chamber. Previous literature has partially addressed their effects on SAGD performance; however, a comprehensive and systematic investigation of the heterogeneous distribution (location, continuity, size, saturation, and proportions) of shale barriers and lean zones is still lacking. In this study, numerical simulations are used to model the SAGD process. Capillarity and relative permeability effects, which were ignored in many previous simulation studies, are incorporated to model bypassed oil. Numerous ranking schemes are formulated to analyze various aspects of SAGD performance. A detailed sensitivity analysis is performed by varying the location, continuity, size, proportions, and saturation of these heterogeneous features. Lean zones and shale lenses (imbedded in a region of degraded rock properties) with different sizes and degrees of continuity are placed in areas above the injector, below the producer, or in between the well pair. It is noted that among numerous parameters that influence the ultimate recovery, remaining bypassed oil, chamber advancement, and heat loss, continuity and position of these features in relation to the well pair play a particularly crucial role. Neural network modeling is subsequently used for constructing data-driven models to identify and propose a set of input variables for correlating relevant parameters or measures, which are descriptive of the heterogeneity and properties of the shale barriers and lean zones, to recovery and ranking results. This work provides a guideline for assessing the impacts of reservoir and saturation heterogeneities on SAGD performance. A set of input variables and parameters that have significant impacts on the ensuing recovery response is identified. One can define readily the proposed set of variables from well logs and apply immediately in data-driven models with field data and scaleup analysis of experimental models to assist field-operation design and evaluation. One can also extend the approach presented in this paper to analyze other solvent-assisted SAGD processes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.146
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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