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Record W1992816694 · doi:10.2118/2004-044

Steam Alternating Solvent Process: Lab Test and Simulation

2004· article· en· W1992816694 on OpenAlexaffabout
L. Zhao, T.N. Nasr, Haiping Huang, Gilles Beaulieu, G. Heck, H. Golbeck

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsProcess (computing)Petroleum engineeringSolventAsphalteneAsphaltProcess engineeringSteam injectionEnvironmental scienceMaterials scienceEngineeringChemistryComputer scienceChemical engineering

Abstract

fetched live from OpenAlex

Abstract Stimulation of carbonate formations by acid dissolution of the rock has been an efficient and successful method of bimproving production in oil and gas wells. Hydrochloric acid is the normal fluid of choice. However, in high temperature applications corrosion issues limit usage, especially in chrome completions. Acetic acid has been used with some success and with adequate corrosion protection. But due to its low reactivity at higher temperatures, the efficiency with which a gallon of acid dissolves the formation is perceived as low. This perception comes from reaction efficiency of acetic acid reported in the literature ranging in values from 90% at 25 °C to 40% at 121 °C for 2 to 15 wt%, respectively. Acetic acid reaction on calcium carbonate is controlled by its small dissociation constant, 1.754E-05 at 25 °C (77 °F) and therefore is labeled a weak acid. Abstract A new heavy oil recovery process, Steam Alternating Solvent (SAS) process, is studied by lab experiments and corresponding numerical simulation. The SAS process involves injecting steam and solvent alternately, using well configurations similar to those in the SAGD process. This process is designed to combine the advantages of the SAGD and Vapex processes to minimize the energy input per unit oil recovered. Lab experiments were conducted using a 2-D highpressure/ high-temperature model. One baseline SAGD test and one SAS test were performed using oil sample from Cold Lake region. Mixture of propane and methane was used as the solvent in the SAS test. The results showed that the energy input in the SAS process was 47% lower than that of the SAGD process, for recovering the same amount of oil. The post-run analysis revealed that asphaltene precipitation occurred in the porous medium. Numerical history matching of the test data using CMG's STARS reservoir simulator captured the main features of the process. Introduction The main obstacle to produce oil from the large deposits of heavy oil and bitumen resources in northern Alberta is the high viscosity of these oils, usually over 10,000 mPa?s at reservoir conditions. There are generally two types of methods for the reduction of oil viscosity. The first is to increase oil temperature by injecting a hot fluid, such as steam, into the reservoir, or by in situ combustion through injection of oxygencontaining gas. The second method is to dilute the viscous oil by injecting low viscosity hydrocarbons (solvent). As the solvent dissolves and mixes with the viscous oil, the low viscosity solvent-diluted oil can then be recovered. Two processes, Steam Assisted Gravity Drainage (SAGD) and Vapor Extraction (Vapex), have been developed for the recovery of heavy oil and bitumen resources (1, 2, 3) based on the combination of the above viscosity reduction methods and the horizontal well technology. The first has been successfully tested in the field and is moving to commercial scale application (4, 5). The second is presently in the initial field-testing stage (6). The advantage of the SAGD process is its relatively higher oil production rate. However the higher production rate is associated with excessive energy requirements, CO2 generation, and post-production water treatment. The Vapex process has the advantage of lower energy consumption, therefore, less CO2 generation. The major drawback of the Vapex process, however, is its relatively lower oil production rate. In the past several years, modifications, such as ESSAGD (7) and SAGP (8), have been proposed to improve SAGD's energy efficiency. In the ES-SAGD process,

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.245
Teacher spread0.233 · 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

Citations73
Published2004
Admission routes2
Has abstractyes

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