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Record W2091988989 · doi:10.2118/2005-075

Chamber Volume/Size Estimation for SAGD Process From Horizontal Well Testing

2005· article· en· W2091988989 on OpenAlexafffund
Abdulftah Shamila, Ezeddin Shirif, M Dong, A. Henniz

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
FundersPetroleum Technology Research Centre
KeywordsVolume (thermodynamics)Process (computing)Computer sciencePetroleum engineeringMechanicsGeologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract Steam-assisted gravity drainage, SAGD, is one of the most recent and the most promising technique in enhanced oil recovery processes. The determination of the swept volume in a steam chamber process provides an early means in which to evaluate the project's progress. The pseudosteady state method has been used to estimate the swept volume, from pressure falloff testing, of horizontal wells. This method is easy to use and is similar in form to the well-known productivity equation for a vertical well. The applicability of the pseudosteady state method in estimating the swept volume for steam injection through a horizontal well was thoroughly investigated. A 3-D thermal numerical simulator was used to investigate the accuracy of the numerically calculated swept volume with the analytically estimated swept volume in SAGD process. In addition, a number of parameters, such as grid blocks number, duration of njection time, permeability ratio(kv kh), steam quality, and the location of the producer, were also studied. Results of this studies show that the pseudosteady state method is valid to use to estimate the swept volume for steam injection through a horizontal well. Within the norms of the independent parameters, the analytically (PSS) estimated swept volume was in good agreement with the numerically simulated swept volume. Introduction In the past, heavy oil or bitumen production has been only marginally economic. However, in recent years, tremendous advances have been achieved in the technology for the recovery of heavy oil or bitumen in both surface mining and in-situ operations. Syncrude and Suncor, the two current mining operations, have been increasing their production and profits consistently, even in the face of varying oil prices. Surface mining currently produces more than 500,000 barrels a day of bitumen. For in-situ recovery, horizontal well technologies have been widely used in both the thermal and non-thermal recovery of heavy oil and bitumen. Primary recovery by conventional production usually yields a low oil recovery in heavy oil reservoirs. Economically feasible technologies, such as steamflooding, steam-assisted gravity drainage (SAGD), and vapour extraction (Vapex) have been developed in order to upgrade large volumes of heavy oil underground (~10 ° to 25 °) API oil1–3. Nowadays, over 80 percent of the total oil produced from all enhanced oil recovery projects in the world is produced by the thermal method. The most economically feasible method of transporting heat to a reservoir, and the flux there of, is by the use of steam. This medium is fully utilized within the areas of steamflooding and SAGD. The Steam-Assisted Gravity Drainage (SAGD) process was successfully tested in AOSTRA's UTF project and has been applied commercially to the Athabasca oil sands and to the Tangleflags' North field. In the usual form of this process, two parallel horizontal wells are placed in the reservoir with a relatively small vertical spacing between them. The top one is used as an injector and the lower one as a producer. As injected steam rises and spreads in the reservoir to form a steam chamber above the injector, heated oil with condensed steam flows continuously downward to the production well byravity4.

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 categoriesInsufficient payload (model declined to judge)
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 score0.095
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.225
Teacher spread0.214 · 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.

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

Citations8
Published2005
Admission routes2
Has abstractyes

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