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Record W2014233960 · doi:10.2118/00-03-03

Waterflooding Under Bottom-water Conditions-An Analytical Model for Two-layer Reservoirs

2000· article· en· W2014233960 on OpenAlexaffabout
Ezeddin Shirif, S.M. Farouq Ali

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringGeologyBottom waterWater injection (oil production)Displacement (psychology)Flow (mathematics)Water saturationSaturation (graph theory)Water flowWater cutPerpendicularMechanicsGeotechnical engineeringPorosityGeometry

Abstract

fetched live from OpenAlex

Abstract Many reservoirs in Alberta and Saskatchewan contain a high water saturation zone (bottom-water) underlying the oil zone. Waterflooding under such conditions is typically ineffective because of channeling 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 bottomwater conditions. Given the reservoir description, the mathematical model should be able to describe the amount of water channeling into the bottom-water, together with a prediction of oil recovery. In this paper, an analytical model was developed to predict waterflood performance when a water zone is present. Results of the mathematical predictions are compared with experimental and simulation results, showing good agreement. Introduction Among the factors influencing fluid flow in layered permeable media is flow from one layer to another in a direction perpendicular to bulk flow. This crossflow may be the result of any or all of the four forces that cause fluid to flow in a permeable medium: viscous forces, capillarity, gravity, and concentration. These driving forces interact with each other in experimental displacements(1,2), making it difficult to credit the observed crossflow to the correct mechanism. This is true to a large extent in simulated displacements(3), but it is possible to simulate displacements with only one effect present(4). Yeung(5) developed a mathematical model for a two-layered reservoir, the lower layer being a water zone to account for crossflow, based on the major assumption that crossflow did not alter the mobility in either layer. It was concluded that crossflow occurred near the injection end and waterflood performance was independent of the point of injection and the injection rate. Hassan(6) modified Yeung's model but used the same assumption. He also developed a semi-analytical model to predict oil recovery performance and calculate the frontal movement of the two flood fronts for bottom-water reservoirs. In view of the foregoing, when a stratified reservoir is being studied for waterflooding, failure to account for crossflow can lead to large errors in oil recovery prediction; however, under bottom-water conditions, this effect is aggravated due to the presence of the mobile water phase. The objective of this chapter is to describe theoretically the effects of the crossflow caused by viscous forces on displacements in a two-layer reservoir, the lower layer being a water zone. This can be accomplished in an unconventional way by solving the flow equations for maximum crossflow, which utilizes the idea of vertical equilibrium solution techniques. The vertical equilibrium concept has been used extensively in the petroleum literature(7-11), mainly as a way to collapse simulations to a lower dimension.

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 score0.061
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.294
Teacher spread0.262 · 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
Published2000
Admission routes2
Has abstractyes

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