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Record W2100616215 · doi:10.2118/2002-192

Optimizing Sand Production Through Horizontal Well Slots in Primary Production

2002· article· en· W2100616215 on OpenAlexafffundabout
Baños Francisco Guillermo Díaz, B. Tremblay, Q. Doan

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsCitationProduction (economics)Computer scienceLibrary scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Cold production, where sand production is encouraged, (CHOPS), has proven to be successful for vertical wells. However, application of CHOPS to horizontal wells has been less profitable due mainly to excessive sand cleanout costs. Therefore, reducing sand cleanout costs by controlling sand production into horizontal wells while enhancing the surrounding permeability of the formation is an important factor in optimizing cold production from unconsolidated heavy oil reservoirs. This paper presents the results of an experimental investigation of the flow of oil and sand in the vicinity of a horizontal well under cold production. Specifically, the experiments physically simulated the flow of oil and sand into a slot in a horizontal well liner. The parameters studied include slot width and sand properties (morphology and grain size distribution). Experimental results suggest that sand production through horizontal well slots can be controlled, depending more on sand grain sorting than on grain morphology or average diameter. The sand cut had a tendency to be higher at the beginning of the sand production period and to decline with time. In most tests, the decline in sand cuts continued until no more sand was produced. Significant changes in the permeability and porosity were determined in the vicinity of the slot. The changes in the parameters were less significant away from the slot. The largest fractions of the sand (< 20 mesh U.S. - 500 µm) have an important role in arch/bridge formation. Introduction Cold production is a primary non-thermal process used in unconsolidated heavy oil reservoirs in Alberta and Saskatchewan, Canada. In this process sand and oil are produced together in order to enhance the oil recovery1–3. A comprehensive review of cold production has been presented by Tremblay et al.1. Hall and Harrisberger4 found that arch stability was rate sensitive at low confining stress but independent of flow rate at high confining stress levels. They observed that angular sand without compaction did not form an arch. When a moderate compaction was applied it could lead to a slightly stable arch. A better interlocking of the surface grains was the explanation for this result. For round sand, arching was not observed for a loose or dense pack at low loads. Yim et al.5 observed that in addition to the flow rate, the arch stability is strongly dependent on the granulometry of the sand and on the size of the perforation. Larger perforations required larger grains to form stable arches. McCormack6 conducted experimental work with spherical particles to determine the arching/bridging mechanism that influences the performance of wire-wrapped sand screens. Selby and Farouq Ali7 showed that sand production increases as the overburden pressure and the fluid flow rate are increased. They also found that spherical small grain sand packs can produce more sand than angular large grain sand packs. Cleary et al.8 observed that the structure of an arch depends on the stress distribution in a sand pack. The cohesive force was shown to have an important role in arch stability when different hydrocarbons liquids were used.

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 score0.093
Threshold uncertainty score0.783

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 head0.025
GPT teacher head0.234
Teacher spread0.209 · 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

Citations4
Published2002
Admission routes3
Has abstractyes

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