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Record W4229728902 · doi:10.2118/2005-161

Liquid Addition to Steam for Enhancing Recovery (LASER) of Bitumen With CSS: Results From the First Pilot Cycle

2005· article· en· W4229728902 on OpenAlexaffabout
R.P. Leaute, B.S. Carey

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsAsphaltProcess engineeringEnvironmental scienceLaserAutomotive engineeringWaste managementComputer scienceMaterials scienceNuclear engineeringEngineeringOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Research aspects for the LASER process were described in a previous 2002 CIM paper (CIM/SPE/CHOA 79011). The research concept has since been field-tested for a single cycle at a pad in Imperial's CSS (Cyclic Steam Stimulation) operations at Cold Lake. The pilot entailed addition of a small 6% volume fraction of C5+ condensate (diluent) into 8 wells during CSS cycle 7. The key pilot objectives were to assess (1) the uplift in Oil-Steam Ratio (OSR) over CSS and (2) the level of diluent recovery. The overall performance of the pilot to date has been encouraging. The diluent recovered is estimated to exceed original expectations and is similar in composition to the injected diluent. The uplift in OSR was consistent with original expectations. Suites of monitoring instrumentation and analytical methods were developed to allow quantification of the recovery of injected diluent. In addition, multivariate analysis (MVA) statistical methods were used to develop a model of CSS process performance. This model reduced the statistical background performance "noise" associated with normal base CSS operations and allowed an improved analysis of the OSR plift in the pilot to be made. Introduction The LASER process has been described in patents issued recently in both the US and Canada1. It essentially consists of combining thermal and solvent effects in a cyclic mode of operations to improve CSS performance. The preferred solvent for LASER is diluent that is already used to pipeline produced bitumen to markets. In cyclic-type operations, the mixing and contacting of solvent with targeted bitumen is expected to be more effective than in continuous thermal operations conducted at constant pressures. Background information on LASER technology is described in previous 2002 paper2. The 2002 paper presenteda description of both CSS and LASER processes for Cold Lakethe laboratory physical proof of principle for the LASER processthe validation of that potential using single-well simulationsa brief outline of the LASER demonstration pilot scope and design facilities. The primary objective of the field pilot was to collect sufficient high quality data to allow accurate assessment of key performance indicators such as (1) bitumen uplift and (2) diluent recovery. Based on the initial simulation work conducted with 6% v/v diluent in steam, performance expectations were for an OSR uplift of 33% over CSS and a recovery of 66% of the injected diluent. Currently, Imperial Oil produces approximately 140,000 bpd of bitumen using the Cyclic Steam Stimulation (CSS) process from its Cold Lake field in east central Alberta. The process is expected to recover approximately 25% of the Original Bitumen in Place (OBIP). Previous work has indicated that if successful, the LASER process could increase this recovery factor by 3–6% OBIP. LASER Pilot Design Pilot Location Selection and Well Layout H22 (LASER Pilot) and H21 (CSS Control) pads are located in the northwestern area of Imperial's operations at Cold Lake. The pads provided the most opportune location to conduct the LASER demonstration field test in 2002.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations41
Published2005
Admission routes2
Has abstractyes

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