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Record W4249495737 · doi:10.2118/2009-207

Improving the SAGD Performance by Introducing a New Well Configuration

2009· article· en· W4249495737 on OpenAlexaffabout
M. Mojarab, T. Harding, B. Maini

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer sciencePetroleum engineeringGeology

Abstract

fetched live from OpenAlex

Abstract Steam Assisted Gravity Drainage (SAGD) is a commercially successful recovery process to produce heavy oils and bitumen. The method ensures both a stable displacement front of steam and economical rates by using gravity as the driving force with a pair of horizontal wells for injection/production. Although several ways of improving the performance have been discussed in the literature, the well configuration employed in the process has remained the same as originally proposed by Butler. A systematic attempt to improve the performance by using radically different well configurations has not been reported. This paper presents a study aimed at examining the applicability of a new well configuration to SAGD process in Athabasca and Cold Lake reservoirs in central and northern Alberta, Canada. The fully implicit thermal reservoir simulator, CMG's STARS 2007, with fully coupled wellbores was used to account for frictional pressure drop and heat losses along the wellbore. 3-dimensional numerical simulation models were set up and sensitivity analyses were conducted to injection pressure. After optimization of the injection pressure an investigation of new well configurations was conducted using these models. The result of this work shows that the SAGD process performance in Athabasca and Cold Lake reservoirs can be significantly improved by changing the well configuration. Introduction Development of horizontal well technology and subsequent improvements in the drilling techniques have made SAGD a reliable recovery method for immobile bitumen recovery [1]. The well configuration employed in vast majority of the field project has been two parallel horizontal wells in the same vertical plane separated by a vertical distance of five meters. The combination of horizontal and vertical wellbores instead of the standard two horizontal wells has also been tested and appears to be a viable option. Joshi compared the SAGD performance for combination of vertical and horizontal injectors-producers, and found that the maximum recovery was obtained from the horizontal well pair [2]. Chung and Butler's [3] experimental observations showed that the rate of bitumen recovery is higher when the steam is injected near the top of net pay, i.e. through vertical injector. Liebe and Butler [4] investigated the effect of well configuration with two types of reservoirs, cold lake and Lloydminster. Chan and Fong [5] examined the offset configuration for heavy oil type of reservoir. Their result showed an additional 5–15% of oil recovery in the case of offsetting or staggered injector. The investigation of effects of well spacing was continued by Ehlig-Economides et al. [6] through thermal reservoir simulation. Sasaki et al [7] found that in a SAGD process, longer well spacing will improve oil production rate and expansion rate of the chamber area. Stalder [8] introduced XSAGD configuration for low pressure reservoirs. It was found that an increase in steam pressure would make this configuration's thermal efficiency worse than that of the standard SAGD configuration. Gates et al. [9] presented the JAGD idea as a new well configuration for reservoirs with vertical viscosity gradient. A higher thermal efficiency than that in the standard SAGD configuration was reported.

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 score0.220
Threshold uncertainty score0.798

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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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
Published2009
Admission routes2
Has abstractyes

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