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Record W2045955696 · doi:10.2118/05-01-05

Cyclic SAGD-Economic Implications of Manipulating Steam Injection Rates in SAGD Projects-Re-Examination of the Dover Project

2005· article· en· W2045955696 on OpenAlexaffabout
G. E. Birrell, A.L. Aherne, D.J. Seleshanko

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsDevon Energy (Canada)
Fundersnot available
KeywordsSteam injectionSteam-assisted gravity drainageAsphaltPetroleum engineeringEngineeringOil sandsEnvironmental scienceWaste management

Abstract

fetched live from OpenAlex

Abstract Operationally, a commercial Steam Assisted Gravity Drainage (SAGD) project differs in a number of respects to other production technologies, such as primary or Cyclic Steam Stimulation (CSS). First, the SAGD technology is unique in that when a SAGD well is shut-in, the recovery process continues to operate. Heat continues to be transferred into the cold bitumen and bitumen continues to drain down into the pool of hot liquids at the base of the chamber. Secondly, the process involves the generation of a large steam chamber, which has considerable heat storage. Short-term variances in steam injection have little impact on chamber pressure and therefore temperature. These unique characteristics present a number of opportunities to exploit seasonal and short-term variations in bitumen and gas prices to the advantage of the SAGD operator. Introduction As of December 2003, the announced or approved SAGD projects in Alberta have a cumulative predicted bitumen production of over eight hundred thousand barrels per day(1). The SAGD process is now clearly moving from an emerging technology to a commercial one. In light of its anticipated widespread application, an important area of study is the economic optimization of the SAGD operating practices. So far, it has been the practice to operate SAGD wells under steady operating conditions. Saturated steam is injected into a horizontal well at high temperatures, typically 200 – 225 ° C, and at pressures between 1.5 – 2.5 MPa. Early attempts were made, in the Dover Pilot Phase A, to determine whether cycles of pressure would lead to additional production rates from the process. It has generally been concluded that, given that cold bitumen (in Athabasca) is essentially a solid, very little effect on SAGD production or recovery is obtained by pressure cycling. For this reason, operators typically maintain a fairly constant chamber pressure over time. This paper contemplates the economic implications of manipulating the steam injection and bitumen production rates to coincide with seasonal variations or temporary extremes in commodity prices. The two commodity prices studied are the price of natural gas, which is used as fuel, and the price of bitumen. Upgraded bitumen is not contemplated in these evaluations. Analytical models are suggested for the determination of the value added, by timing of steam injection and bitumen production. A simulation model of Phase B at the Dover Project was used to estimate how Phase B would have reacted to variable injection and production. The Dover Project is the home of the first test of classical SAGD. The first commercial pilot, Phase B of the Dover Project, drilled in 1993, consists of three 500 m long horizontal well pairs and is still on production. Yee and Stroich provide a complete discussion of the historical Phase B operations(2). Markets and Price Variation Natural Gas The price of natural gas varies by season. The demand for gas in the North American market is much higher in the winter months because gas is used for heating.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.963
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2005
Admission routes2
Has abstractyes

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