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Record W2010325671 · doi:10.2118/01-06-03

The Wizard Lake Vertical Miscible Flood Solvent Bank Redesign Concept

2001· article· en· W2010325671 on OpenAlexafffund
M.Y. Kwan, J.P. Batycky, Jine Tang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsImperial Oil (Canada)
FundersUniversity of Waterloo
KeywordsInjectorPetroleum engineeringSolventMiscibilityDiffusionFlood mythResidence time (fluid dynamics)Environmental scienceGeologyMaterials scienceMechanicsChemistryGeotechnical engineeringEngineeringThermodynamicsOrganic chemistryMechanical engineeringPolymerPhysicsGeography

Abstract

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Abstract The Wizard Lake D-3A Pool has been undergoing a tertiary hydrocarbon miscible flood (HCMF) since 1983 after completing primary production (1951 - 1967) and a secondary HCMF (1969 - 1983). The operation is comprised of three solvent injectors, four push gas injectors, and 42 production wells. Fresh solvent injection (2,500 m3/day) was initiated in 1991 to make up for significant solvent coning and changed the flood design from a vertical flood controlled by longitudinal diffusion to a horizontal flood dominated by transverse diffusion. Recycling in this case also allows the old propane-plus bank to be replaced by a more economical ethane-plus blend. Under solvent recycling, the mixing zone would be stabilized (balanced injection and withdrawal) with its thickness increasing from zero at the injector to the maximum at the producer. The design problem is thus reduced to finding the solvent residence time at each producer and the corresponding minimum bank thickness needed to maintain miscibility. The 1D diffusion equation coupled with miscibility data was used to calculate the required bank thickness and a full field 2D streamline model was used to predict the solvent residence times in this study. New solvent bank thickness design curves were generated by merging the residence times and the minimum miscible bank thickness data. Operating strategies based on the new design curves can potentially reduce the 1994 solvent bank size of 5.5 million reservoir cubic metres (rm3) to 3.0 million rm3. The concept applied in Wizard Lake can be extended to other miscible floods that involve solvent recycling. TABLE 1: Wizard Lake solvent bank design history. Background The Wizard Lake D-3A Pool is a dolomitized carbonate reef with a current estimated original-oil-in-place of 62.4 million m3. Reservoir pressure was decreased from 15.65 to 12.95 MPa during primary production. Prior to the secondary miscible flood (1968), water was injected to repressurize the reservoir to 14.80 MPa. At this time, the water-oil contact rose to 1,201 m sub-sea (mSS) from its initial position of 1,229.6 mSS. A first-contact miscible (FCM) propane-plus solvent and a 95 ﹪ methane push gas were injected to sustain a vertical miscible flood from 1969 to 1983. The solventoil contact reached the 1,201 mSS depth by 1983 and a new approval was granted to extend the flood to capture the tertiary oil trapped between the 1,201 and 1,229.6 mSS levels. This stage is known as the tertiary extension phase and the solvent was changed to an ethane-plus blend. Figure 1 shows a well map of the Wizard Lake D-3A Pool. Table 1 chronicles the history of the solvent bank design for the Wizard Lake flood. Extensive solvent coning commenced in 1991 (∼ 3:1 solvent to oil ratio). The produced solvent was purified and reinjected in order to maintain solvent bank integrity. Updated geological data indicated that the targeted reservoir had a lower porosity than was estimated originally. Due to solvent re-injection, the reservoir flow became largely horizontal with solvent mixing controlled by transverse diffusion. These events prompted the questioning of the original design assumptions and led to a redesign of the flood in 1994.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2001
Admission routes2
Has abstractyes

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