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Record W4244631238 · doi:10.2118/2008-007

Prediction of Carbon Dioxide Production in SAGD Operations

2008· article· en· W4244631238 on OpenAlexaboutno aff
H.F. Thimm

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon dioxideProduction (economics)Petroleum engineeringEnvironmental scienceComputer scienceProcess engineeringWaste managementGeologyEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract In 2007, work was published on the predictability of hydrogen sulphide production in Athabasca SAGD projects. It was possible to predict the hydrogen sulphide production per unit volume of bitumen produced against steam zone temperature, by assuming pseudo-zero order kinetics, using the Arrhenius energy published by Strausz and his co-workers at the University of Alberta. An examination of field results for carbon dioxide production in Athabasca projects shows that the predictability of carbon dioxide is less simple. The carbon dioxide production is not erratic but goes through a distinct temperature minimum, an important result in view of the dependence of silica production in SAGD and scaling behaviour in facilities on carbon dioxide. Again, as for hydrogen sulphide production, it is possible to estimate carbon dioxide production by a simple graphical technique, which is shown. Partial explanations for the observed temperature minimum are offered. Introduction The production of acid gases in SAGD projects is a matter of some importance. A number of operational characteristics depend on the presence of carbon dioxide as produced via dissolution in produced water. It has been shown previously1 that silica production is strongly dependent on dissolved carbon dioxide. There is likewise evidence that aggressive degassing of produced water has caused unwanted scales of silicate minerals, such as talc, chrysotile and tremolite, to be deposited in various parts of the facilities2. A simple production diagram for hydrogen sulphide in SAGD was recently reported by Thimm3. The hydrogen sulphide produced per unit bitumen could be described as a simple Arrhenius-type relationship with steam zone temperature, using an activation energy of 56220–65920 kJ/mole (13450 to 15770 kcal/mole). The assumption of pseudo-zero order kinetics yielded a smooth curve which fitted the field data for most SAGD projects in Athabasca satisfactorily. The behaviour for carbon dioxide was previously expected to be similar, with an Arrhenius activation energy of 17520 kJ/mole (4192 kcal/mole)4. Field Results The results from field measurements of carbon dioxide generation in Athabasca SAGD projects show a very different situation from that which might have been expected from the Strausz value of a single activation energy. Figure 1 shows an upper curve, which, owing to the low activation energy reported by Strausz from laboratory measurements, is almost straight. This curve was the line expected from use of the data by Strausz. The curve actually obtained in field measurements between 180 and 215 degree steam zone temperatures is the lower curve, with field data shown in bold triangles. Surprisingly, the true production curve appears to go through a distinct minimum at approximately 195 degrees Celsius. As with the corresponding hydrogen sulphide curve in Figure 2, a first estimate of anticipated carbon dioxide production per m3 bitumen may be read off the graph. Since the true production curve seems to rejoin the Strausz curve (upper line), production values above 215 ° C may probably be read off the extrapolation of the Strausz curve. However, the reader is cautioned that few field data in this higher temperature range have been published.

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 score0.064
Threshold uncertainty score0.994

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 head0.031
GPT teacher head0.238
Teacher spread0.207 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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