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Record W4205324715 · doi:10.2118/2005-115

World's First SAGD Facility Using Evaporators, Drum Boilers, and Zero Discharge Crystallizers to Treat Produced Water

2005· article· en· W4205324715 on OpenAlexaboutno aff
W.F. Heins, R. McNeill, S. Albion

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsCitationEngineeringWaste managementEnvironmental scienceLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Steam Assisted Gravity Drainage (SAGD) heavy oil recovery facilities have traditionally used a combination ofwarm or hot lime softening, filtration, and weak acid cation (WAC) ion exchange to pretreat de-oiled produced water. The pretreated water is directed to "once through" steam generators (OTSG) to produce 75–80% quality steam. The steam-water mixture goes through a series of vapour-liquid separators to produce the 100% quality steam required for injection into the oil well. The steam fluidizes the heavy oil andallows the oil/water mixture to be brought to the surface. The oil is recovered as product and the produced water is de-oiled and treated for reuse in the OTSG. An alternative method of produced water treatment and steam production, which has recently been implemented in Alberta by Deer Creek Energy, is mechanical vapour recompression evaporation followed by standard drum boilers. This method of SAGD steam production is much simpler to operate, is more cost effective, and results in significant increases in equipment reliability, on-stream availability and, ultimately, in increased oil production. In conjunction with this process, Deer Creek Energy has taken the additional step of recovering all liquid waste streams for reuse in the plant, resulting in zero liquid discharge (ZLD). Designing the facility for ZLD eliminates the need for deep well injection, minimizes make-up water requirements, and simplifies the permitting process. Introduction Water treatment and steam generation methods for heavy oil recovery processes have rapidly evolved over the past few years. Traditionally, once-through steam generators (OTSG) have been used to produce about 80% quality steam (80% vapour, 20% liquid) for injection into the well to fluidize the heavy oil. However, the relatively new heavy oil recovery method referred to as steam assisted gravity drainage (SAGD), requires 100% quality steam for injection. In order to allow the continued use of OTSG for SAGD applications, a series of vapour-liquid separators is required to produce the required steam quality. For both SAGD and non-SAGD applications, pretreatment of the OTSG feedwater has consisted of silica reduction in a hot or warm lime softener, filtration, and hardness removal by weak acid cation (WAC) ion exchange. In most cases, the OTSG blowdown is disposed of by deep well injection. A simplified block flow diagram of this traditional approach to produced water treatment and steam generation is provided in Figure 1. As the use of the SAGD process became increasingly common for heavy oil recovery in Alberta and worldwide, the traditional methods of produced water treatment and steam generation were re-evaluated to determine whether other alternative methods may provide more technically and economically viable solutions. This paper compares the use of traditional methods with an alternate method being implemented in Alberta; the use of falling film, vertical tube vapour compression evaporation for produced water treatment, standard 100% quality drum boilers for steam production, and zero liquid discharge (ZLD) crystallizers to eliminate the need for deep well injection. Both technical and economic criteria are presented. This integrated approach is currently being implemented by Deer Creek Energy for the Joslyn Phase II SAGD facility located near Fort McMurray, Alberta.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.991

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)0.0100.001

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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 designNot applicable
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

Citations3
Published2005
Admission routes1
Has abstractyes

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