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Record W2047635980 · doi:10.2118/77965-ms

Waterflooding Performance of Stratified Reservoirs with Bottom-Water Conditions

2002· article· en· W2047635980 on OpenAlexaff
Ezeddin Shirif, Gary Zao

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPetroleum engineeringBottom waterGeologyWater cutWater injection (oil production)Saturation (graph theory)Water saturationEnvironmental scienceGeotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract Many reservoirs in North America contain a high water saturation zone "bottom-water" underlying the oil zone. Waterflooding under such conditions is typically ineffective because of chanelling of water through the bottom-water zone. However, in some cases, waterflooding such reservoirs may still be feasible and economically viable. While there is no doubt that some of the injected water bypasses the oil zone through the bottom-water zone, most of the injected water may still displace the oil, depending on the reservoir conditions. Therefore, a mechanistic understanding of oil displacement by a waterflood in the presence of a bottom-water zone is the basis for predicting recovery performance, and there is a need for developing a mathematical model to describe water channeling under bottom-water conditions. In this paper, a new simple analytical model was developed to predict waterflood performance in stratified reservoirs when a water zone is present. Given the reservoir description, the new mathematical model can be used to describe the amount of water channeling into the bottom-water, calculate pressure distribution in each layer and together with a prediction of oil recovery. Results of the mathematical predictions are compared with experimental and simulation results, showing good agreement.

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)0.000
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 score0.438
Threshold uncertainty score0.388

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 head0.022
GPT teacher head0.223
Teacher spread0.201 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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