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Record W1971480457 · doi:10.2118/2006-157-ea

Automated Global Optimization of Commercial SAGD Operations

2006· article· en· W1971480457 on OpenAlexaffabout
Colin Card, Neilin Chakrabarty, Ian D. Gates

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer sciencePetroleum engineeringManufacturing engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The economics of a SAGD operation depend on several factors including the oil rate, cumulative production/well pair (recovery), and cumulative SOR. Complete economic optimization of the process would need to consider all these and possibly additional factors. To date, so far as the authors are aware, economic optimization of a commercial SAGD operation has been carried out by trial and error methods, using many simulations in an attempt to identify the optimum approach to steam use (steam optimization). There is considerable interest in automated ‘goal seeking’ optimization approaches which can largely eliminate the tedious and repetitive work involved in running a large number of simulations to find an optimum case. These approaches seek to find a global optimum of some objective function which is related to the economic criteria for the project. Because of the size, and run time requirements, of commercial SAGD simulation models, ‘brute force’ optimization approaches, where hundreds of runs are made, are neither practical nor elegant. More sophisticated approaches are required. The authors have developed techniques which allow both the automation of the manual approach, and the optimization of this automation. That is to say, the optimization approach used not only finds the global optimum for a given objective function but also finds this global optimum in an optimal fashion. In the work described here, we have chosen to use cumulative SOR as a surrogate for the economic objective function to be optimized. For demonstration purposes the commercial SAGD model used has been restricted to a single well pair. Introduction Steam-Assisted Gravity Drainage (SAGD) has proven itself as a commercial heavy oil and bitumen recovery technology in the Athabasca and Cold Lake regions of Alberta (Komery et al., 1999; Butler, 1997; AED, 2004; Yee and Stroich, 2004; Scott, 2002). SAGD, displayed in cross-section in Figure 1, was developed by Butler (1997) while at Imperial Oil in the late 1970s and consists of two substantially parallel horizontal well bores, one positioned above the other. Typically, the inter-well bore distance is between 5 and 10 m, the wells are between 500 and 1000 m long, and inter-well pair spacing is between 90 and 120 m. (Singhal et al., 1998; Komery et al., 1999). The production well is located a few meters above the base of pay. Steam injected into the top well enters the steam chamber, flows convectively throughout the chamber, and eventually loses its latent heat to the oil sand along the edge of the chamber. As a consequence, the oil's temperature rises, its viscosity falls, and under the action of gravity, it flows either vertically from the top of the chamber or down the edges of the chamber towards the production well located at the bottom of the chamber. To start the process, in common practice, steam is circulated within the injector and producer. In effect, they act as line heat sources which conductively heat the regions around and between the wells. After the oil between the wells is sufficiently warm, the wells are converted over to SAGD mode.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.258
Teacher spread0.244 · 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 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

Citations12
Published2006
Admission routes2
Has abstractyes

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