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Record W2087667192 · doi:10.2118/03-02-03

Combined Polymer and Emulsion Flooding Methods for Oil Reservoirs With a Water Leg

2003· article· en· W2087667192 on OpenAlexaffabout
H.J. Abdul, S.M. Farouq Ali

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringEmulsionOil in placePetroleumGeologyWater injection (oil production)Water floodingSaturation (graph theory)Environmental scienceInjectorWater cutEngineeringChemical engineering

Abstract

fetched live from OpenAlex

Abstract Many light and medium gravity oil reservoirs have an underlying contiguous water zone, in communication with the oil zone. As a result, a conventional waterflood is often unsuccessful because the injected water tends to channel into the more conductive bottom water layer. The research results discussed here show that modified waterfloods of such reservoirs may still be economically viable. Experiments were carried out in a three-dimensional model, employing a number of techniques, including horizontal wells. The flooding fluids consisted of polymer solutions and emulsions. The most successful strategy was to use a 10% quality oilin- water emulsion as a blocking agent, and a polymer solution as the mobility control fluid. Such a combination yielded oil recoveries approaching 70%, as compared to 50% for a conventional waterflood, for equally thick oil and water layers. The experimental results were correlated by means of a threezone analytical model allowing for crossflow between oil and water layers, which is useful for predicting the performance of such floods. Experiments utilizing horizontal injector-producer pairs for conventional waterfloods in the presence of a water leg, as well as floods utilizing polymers and emulsions showed only limited gains over vertical well pairs. Guidelines are offered for the choice of well and fluid combinations for successful floods. Introduction Waterflooding is a relatively inexpensive secondary recovery method that is used widely in the petroleum industry. In the provinces of Alberta and Saskatchewan a number of light and moderately heavy oil reservoirs contain a high water saturation zone in communication with the oil zone. Under conventional waterflood such reservoirs have been observed to show poor performance. The major reason for this is an insufficient and incomplete sweep of the reservoir by the injected water, which tends to move to the producing wells through the more permeable portions of the reservoir. This results in low recovery. Several laboratory model studies have been undertaken to investigate the effect of various parameters on oil recovery in bottom water reservoirs(1–8). High water cuts and rapidly decreasing oil rates early in the production life of such reservoirs have, in many instances, prompted their suspension or abandonment at very low levels of recovery(2). Mobility ratio is perhaps the single most important parameter in waterflooding bottom water reservoirs. A number of flooding fluids have been used to control mobility ratio. This paper examines effective techniques to waterflood bottom water reservoirs using polymer and emulsion as mobility control and/or blocking agents. The effect of vertical and horizontal injectors and different combinations were also investigated. Experimental Set-up and Procedure A diagram of the experimental apparatus is shown in Figure 1. The apparatus is made up of two constant rate pumps and a specially- designed aluminum core holder with a rectangular crosssection. The inside dimensions of the core holder were 5.08 cm (2.0 in.) wide, 7.62 cm (3.0 in.) deep and 122 cm (48 in.) long. Two pumps were used for simultaneous injection of two different fluids to simulate a vertical displacement front. The injection well was specially designed to allow the simultaneous injection of two different fluids.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.522

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 integrity0.0000.000
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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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 designBench or experimental
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

Citations24
Published2003
Admission routes2
Has abstractyes

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