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Record W2481348055 · doi:10.2118/2002-171

Simulating Cold Production by a Coupled Reservoir-Geomechanics Model With Sand Erosion

2002· article· en· W2481348055 on OpenAlexaffabout
Y. Wang, Shifeng Xue

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsGeomechanicsErosionGeologyPetroleum engineeringGeotechnical engineeringEnvironmental scienceSoil scienceGeomorphology

Abstract

fetched live from OpenAlex

Abstract A fully coupled reservoir-geomechanics model is developed to simulate the enhanced production phenomena both in heavy-oil reservoirs (i.e. Northwestern Canada) and conventional oil reservoirs (i.e. North Sea). The model is implemented numerically by fully coupling an extended geomechanics model to a two-phase reservoir flow model. A sand erosion model is postulated after the onset of sand production, which is determined based on the degree of plastic deformation inside the reservoir formation calculated by the coupled reservoir-geomechanics model. Both the enhanced production and the ranges of the enhanced or sanding zone are calculated, the effect of solid production on oil recovery and enhancement are analyzed. Our studies indicate that the enhanced oil production can be contributed by a combined effect of higher fluid velocity due to the movement of the sand particles according to the modified Darcy's flow and an effective well radius increase or negative skin development due to sand erosion. Despite of such an improvement on mobility may reduce the near well pressure gradient so that the sanding potential is weakened, it permits an easier path for oil to flow into the well due to an enhanced permeability. Two-phase flow can affect pressure gradient and formation residual cohesion due to capillary pressure buildup. Indirectly, production enhancement strategy can be controlled by the water saturation distribution and development, as the success and economic value of a field operation can depend on whether sand production can be induced or not. Such an analogy can also be used for a completion strategy by allowing a certain amount of sand production before sand control strategy implemented in high flow-rate reservoir, when the optimum production is desirable and when the reservoir productivity does not vitally rely on sand production. Introduction Sand production is a phenomenon that occurs during aggressive production induced by a combined impact of viscous fluid flow and the in-situ stress concentration near a wellbore and perforation tips in poorly cemented formations. Such a solid production compromises oil production, increases completion costs, and reduces the life cycles of equipment down hole and on the surface. Sand production has been a major concern to production engineers for decades, either in poorly consolidated reservoirs or from those formations with cement. These sanding effects often are associated with high fluid viscosities and production rates, and the issue is becoming more critical these days as operators are following more aggressive production schedules. Sand production, on the other hand, has been proven a most effective way to increase well productivity both in heavy oil and light oil reservoirs1,7. A typical 4–10 fold increase in oil production is normal in heavy oil reservoirs (Cold Production)1–3, and up to a 44% increase in sand-free rate after a certain amount of sand production in conventional oil reservoirs has been reported8,9. For conventional oil producers, both enhanced production and improved sand-free rate are highly desirable. Whereas for the heavy crude operators, other than the improved productivity, operating cost reduction is vital for a profitable operation, because the price margin between heavy oil and light oil is high (this is particularly important for cold production operators in northwestern Canada).

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 score0.069
Threshold uncertainty score0.998

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 head0.015
GPT teacher head0.203
Teacher spread0.187 · 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

Citations2
Published2002
Admission routes2
Has abstractyes

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