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Record W4247605217 · doi:10.2118/2008-149

Experimental and Economic Analysis of the Thermal Solvent and Hybrid Solvent Processes

2008· article· en· W4247605217 on OpenAlexaff
T.W. Frauenfeld, C. Jossy, J. Bleile, D. Krispin, J. Ivory

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSolventThermalThermal analysisProcess engineeringComputer scienceChemistryMaterials scienceThermodynamicsPhysicsEngineeringOrganic chemistry

Abstract

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Abstract Several partially scaled laboratory model experiments were conducted to evaluate a hybrid solvent-steam process for recovery of heavy oil or bitumen. All experiments used Athabasca UTF bitumen, and modeled a 30 meter thick formation. The experiments were compared using a common set of economic assumptions. The experiments showed that a hybrid solvent-steam process could recover bitumen at steamoil ratios much lower than those observed for SAGD, and achieve reasonable ultimate oil recovery (60% IOIP). The economic analysis based on experiments indicated that a hybrid solvent-steam process could be more cost-effective than SAGD for a 30 m Athabasca formation. Introduction Some heavy oil reservoirs are difficult to produce by cold production. The oil may be immobile at reservoir temperature, or there may be some initial oil mobility and some reservoir drive energy, but the sand strength precludes the production of wormholes. These reservoirs may contain dead oil, as in the case of Athabasca bitumen, or they may have some dissolved gas, as in the case of Cold Lake or Burnt Lake reservoirs. SAGD is the main commercial technology used for in-situ recovery of these oils. Due to the increasing costs for energy (natural gas) and the increasing restrictions on fresh water usage, solvent-based processes (VAPEX, Thermal Solvent, Hybrid Solvent, N-Solv, Savex) have been proposed as alternative technologies for heavy oil and bitumen production. Most of these technologies utilize a pair of horizontal wells, similar to those used in SAGD, but use a gaseous solvent, typically propane, alone or in conjunction with steam, to recover the oil. The VAPEX process may be augmented by adding heat. Heating of a horizontal wellbore will reduce bitumen viscosity sufficiently to produce a large increase in oil production rate. The heat also serves to initiate communication between theinjector and the producer. The heat also serves to speed the iffusion of solvent into the oil. The combination of heated wellbores and VAPEX is known as the Thermal Solvent process. In the Hybrid Solvent Process, a pair of horizontal wells is used, but the injector wells co-inject steam and solvent. The heat vaporizes the injected solvent. Solvent vapour and steam move to the oil interface at the edge of the vapour chamber and condense. Solvent dissolves in the oil. The diluted oil is reduced in viscosity and flows down the edge of the vapour chamber to the production well. The vaporized solvent is flashed out of the oil as it enters the heated area near the production well. The vaporized solvent then returns to the vapour chamber, where it mobilizes additional oil. The description of the Thermal Solvent process is found in Ivory et al (Ref. 1) and in Frauenfeld et al (Ref. 2). The Hybrid Solvent process is described in Frauenfeld et al (ref. 3). A series of experiments was performed to model recovery processes where steam and solvent injection was combined. Because of the low thermal mass of solvent, it is expedient to heat the solvent by co-injection with steam. The result is a Hybrid Solvent process (Figure 1).

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 designBench or experimental
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

Citations5
Published2008
Admission routes1
Has abstractyes

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