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Record W2087376792 · doi:10.2118/159424-ms

Reserve Growth an Examination of Infill Drilling and EOR/IOR in Canada

2012· article· en· W2087376792 on OpenAlexaboutno aff
Richard Baker, Rous Dieva, Kerry Sandhu

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2012
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInfillDrillingPetroleum engineeringProduction (economics)Enhanced oil recoveryFossil fuelOil productionStructural basinGeologyDrilling fluidEnvironmental scienceEngineeringCivil engineeringPaleontologyWaste managementEconomics

Abstract

fetched live from OpenAlex

Abstract Growth in reserves from existing reservoirs has been the primary contributor to reserve additions in most mature basins. Historically, infill drilling has been the main driver for reserves growth for the Western Canadian Sedimentary Basin (WCSB) and other parts of the world. This study examines the other why's of reserve growth in WCSB. Examination of historical trends in fields shows that injection processes (Enhanced Oil Recovery, waterflooding) dominate reserves growth in WCSB, and have been the reason for an increasing oil rate in the region. This paper will examine performance of both legacy production and infill well drilling programs and relate it to drive mechanism. Production trends in Canada will be presented as well as the incremental production and contribution to WCSB seen from the infill programs. Next, EOR development will be analyzed. Again, production trends in Canada will be presented and incremental oil contribution due to each individual EOR method will be shown. A brief look at USA EOR experience will also be analyzed. The influence of technology growth and oil price will be reviewed for both infill and EOR oil pools.

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 score0.065
Threshold uncertainty score0.977

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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