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Record W2045539237 · doi:10.2118/94630-ms

Simple Thermal Efficiency Parameter as an Economic Indicator for SAGD Performance

2005· article· en· W2045539237 on OpenAlexaff
Hyundon Shin, M. Polikar

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSimple (philosophy)Metric (unit)Economic indicatorThermalComputer scienceTwo stepEconomic efficiencyEconomic analysisEconomic potentialEconomic evaluationValue (mathematics)Mathematical optimizationEconometricsMathematicsApplied mathematicsThermodynamicsEngineeringEconomicsPhysicsMicroeconomicsMacroeconomicsOperations managementMachine learning

Abstract

fetched live from OpenAlex

Abstract A new economic indicator, called simple thermal efficiency parameter (STEP), was developed to evaluate the performance of a SAGD project. STEP is based on CSOR, CDOR and RF for the time corresponding to SOR = 4. STEP was found to be a useful quantitative economic criterion, with a value of 1 for an economic case (CSOR of 3, CDOR of 0.111 m3/d/m of horizontal well length and RF of 0.5). To validate this new economic indicator, STEP was calculated based on data from two published studies in which SAGD related simulations were performed. The first study, which provides no economic parameters, shows that most of the optimal cases always have the highest STEP values. The second study, which provides economic parameters (ROR and NPV), shows a good linear relationship between ROR and STEP. STEP is greater than 2 in the case of the economic SAGD scenarios. This study has validated the usefulness of STEP as an economic indicator. STEP can be used as a financial metric quantitatively as well as qualitatively for this type of thermal project.

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)0.001
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 score0.010
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations6
Published2005
Admission routes1
Has abstractyes

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