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Record W4323655315 · doi:10.2118/212755-ms

Life Cycle Analysis (LCA) and Carbon Intensity of In-Situ Reflux (ISR) Process Using Renewable Solvents

2023· article· en· W4323655315 on OpenAlexaffabout
Mohammad Zeidani, Zhangxin Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasRenewable energyEnvironmental scienceProcess engineeringWaste managementPropaneLife-cycle assessmentElectricity generationPetroleum engineeringEngineeringChemistryPower (physics)Electrical engineeringProduction (economics)Organic chemistry

Abstract

fetched live from OpenAlex

Abstract In-Situ Reflex (ISR) is a novel solvent-based process that utilizes resistive electric heaters to vaporize solvent and recycle mobilized water downhole. ISR promises a significant reduction in greenhouse gases emissions through the elimination of steam generation and water handling facilities at the surface as well as effectively vaporizes the injected fluid along a wellbore. However, the economic viability of this process is highly dependent on the in-situ refluxing of the solvent and the clean fuel regulations which requires an in-depth understanding of the process and associated challenges environmentally and economically. In-situ recovery processes are mainly known as greenhouse gas emission intensive under SAGD operation, while there is an excessive bitumen reserves that can be only recovered by In-situ method. Solvent co-injection with steam alternative processes, including ISR, are potential solution to reduce the greenhouse gas emission of In-situ recovery processes, however, Life Cycle Analysis (LCA) hasn't been carried out in the same level as the effort to prove the technical feasibility on these alternative processes. The performance of the process didn't alter as results of switching from propane to renewable propane however, the CI associated with two scenarios was improved by almost 35% when renewable propane was used. The GHG model indicated power to heaters, source of the power generation units, solve to water ratio and solvent type are determining factors in calculating the carbon intensity of the ISR. Close approximate of the operating area to the refining infrastructure has impacted the CI score of the ISR process which puts Alberta in advantage to other provinces. The proposed model brings an insight into GHG intensity of the ISR process with the aim of increasing the understanding of clean fuel regulation, along with identifying the advantages and limitations of using the bottom-hole resistive heater technology. This will lead to a higher predictability of successful field implementation, lower upfront capital cost taking advantage of future carbon credits, higher energy efficiency, and environmentally sustainable development Recently, by taking advantage of the opportunity, the majority of municipalities across the nation are already in a very strong position to address a portion of their GHG emission concerns by converting readily available biogas into functional RNG. Methane can be separated or upgraded from other naturally existing main ingredients like water and CO2 to produce biogas, which typically comprises between 50% and 60% methane. The resulting cleaned-up gas, also known as biomethane or RNG, has a chemical makeup that makes it appropriate for injection directly into the local gas utility distribution system without the need for any additional adjustments. Any gas device that is currently in use (such as furnaces, hot water heaters, boilers, CNG automobiles, etc.) can be used to burn RNG. It has value as a physical good (gas for consumption like traditional natural gas) as well as a carrier of environmental qualities. Because of this, the value of RNG is higher than the value of conventional natural gas.

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: Simulation or modeling · Consensus signal: none
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)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.317
Teacher spread0.288 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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