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Record W2031402312 · doi:10.2118/2004-265

Characterization and Role of Reservoir Heterogeneity in Performance of Infill Wells in Water Flood and Miscible Projects

2004· article· en· W2031402312 on OpenAlexaboutno aff
A.K. Singhal, S.J. Springer

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInfillFlood mythReservoir modelingPetroleum engineeringCharacterization (materials science)GeologyGeotechnical engineeringEngineeringCivil engineeringMaterials scienceArchaeologyGeography

Abstract

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Abstract Reservoir heterogeneities are known to influence performance of infill wells in water flood or miscible flood projects to the extent of determining their success or failure, but no satisfactory ways currently exist for their characterization, quantification or prediction. In 1950, Dykstra and Parsons1 presented empirical correlations for performance of water floods in certain California reservoirs, based on observed variation in permeability. In general, procedures for most reservoir engineering predictions could be greatly simplified if heterogeneity could be characterized by one or more terms. However, it is painfully obvious that because of the multitude of reservoir descriptions encountered in any oil prone basin, a single parameter (or a small number of parameters) may not be adequate. This is because of different ways in which heterogeneity impacts the performance of vertical or horizontal infill wells placed to enhance production/ reserves in a variety of water flood/ miscible flood situations. In recent years, a large number of infill wells were placed in various water flood and miscible flood projects in the Western Canadian Sedimentary Basin (WCSB). Performance of both horizontal and vertical infill wells varied widely in terms of initial oil rates and reserves. They also resulted in mixed economic successes in that many of these infill wells were unable to payout. In general, horizontal infill wells drained about twice the amount of oil as contemporaneous vertical infill wells placed in the same reservoirs. However, in spite of this, they may sometimes be less attractive on a risk-weighted basis, because of their higher cost. It is postulated that reservoirs are divided into predominantly horizontal or vertical 'compartments' due to spatial variations in reservoir attributes such as permeability, thickness, environment of deposition (stratification, shaliness) and post-depositional changes (structure, fractures, digenesis). Oil contained in these compartments, though often not strictly isolated, is not easily contacted or displaced by injected water/ gas. Alternately, flow within the reservoir might be predominantly through certain pathways, some of which might involve cross-flow between various intervals. Thus horizontal wells will do a better job of draining oil than vertical wells in certain situations and, vice-versa. However, in reservoirs with certain kinds of heterogeneity, distribution of mobile water and/or gas in an ongoing water flood or miscible flood project might make horizontal wells more vulnerable to prematurely watering/ gassing out and hence, more risky. This paper presents a few case studies of infill wells in water flood and miscible flood projects in WCSB. Comparative performance is presented for infill horizontal and vertical wells placed in these projects during the past fifteen years. Through a review of statistical data, certain observations are made regarding heterogeneity and its relation to performance of horizontal wells. Finally, inferences are generalized to obtain clues to situations where horizontal or vertical infill wells may be more appropriate. Introduction In water flood or EOR recovery projects, recovery at any point in time is a product of displacement efficiency, conformance (vertical sweep efficiency) and areal sweep efficiency. Whereas conformance could be estimated using Dykstra-Parson's procedures1, sweep efficiency largely depends on areal heterogeneity in different intervals, besides factors such as mobility ratio, injected fluid throughput and flood pattern geometry2

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.976

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 head0.008
GPT teacher head0.195
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 designObservational
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
Published2004
Admission routes1
Has abstractyes

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