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Record W4392726860 · doi:10.2118/218041-ms

New Insights From an Old Method After History Matching a Newly Designed 1-D Cyclic Steam Stimulation Experiment

2024· article· en· W4392726860 on OpenAlexaff
Belenitza Sequera-Dalton, D. Gutiérrez, R.G. Moore, S. A. Mehta, M.G. Ursenbach, Hector Garcia, R. Pérez, H. A. Rodríguez, Eduardo Manrique

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of CalgaryAlberta Energy
Fundersnot available
KeywordsStimulationMatching (statistics)Computer scienceMathematicsPsychologyNeuroscienceStatistics

Abstract

fetched live from OpenAlex

Abstract A cyclic steam stimulation (CSS) laboratory experiment was conducted with dead heavy oil. Four cycles of steam injection and fluid production were performed, at reservoir pressure, in order to assist in the numerical modelling and understanding of the main mechanisms involved in the process. This was an important part to developing a base model for a broader project evaluating CSS steam-hybrid experiments with live oil. Experimental data, history matching approach and results, as well as key insights are presented. An experimental setup, originally designed to evaluate CSS hybrid processes, was improved by fitting a sight glass to identify the fluids flowing out of the opposite core end (into a ballast system), during injection cycles. Dead oil was used to facilitate the analysis of this experiment. Relative permeability curves were tuned to history match each cycle sequentially. Injection periods were matched before production ones in order to estimate the amounts of oil and water displaced to the ballast during injection (unknown although total liquid volumes in the ballast were continuously recorded), which were later injected back into the core during production periods. A one-dimensional grid successfully represented the core section while the ballast system was modelled with a production and an injection well. Experimental data such as temperature profiles, pressures and rates were honored. A volumetric ratio of 40% water and 60% oil appeared to be the typical composition of the fluid received by the ballast during injection periods based on simulation results. Fluids reinjected from the ballast back into the core were modelled as an emulsion (i.e., a water-oil mixture). Relative permeability curves were the same for injection and production periods within the same cycle, except for an increased critical water saturation during the last two production periods. One set of relative permeability curves was obtained for each of the four cycles, and are presented in this work. The need to have different curves per each cycle suggests a different flow mechanism was taking place during the CSS test. It appears that the injected steam, after condensing to water, partially emulsified with the heavy oil in the core. Although all the cycles of the CSS experiment were successfully matched using water-oil relative permeability curves, questions about their sufficiency to model heavy oil recovery with steam processes arise. New insights are discussed based on this work and available literature. A CSS experiment conducted on a recently commissioned CSS laboratory setup, that mimics the cyclic movement of reservoir fluids with a ballast system, was successfully history matched using a non-traditional approach. The fluids displaced out of the core-into the ballast-during steam injection were re-injected as a water-oil emulsion. New insights from this work underline the need to rethink the traditional way of modelling heavy oil recovery with steam, where emulsion formation typically occurs.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.343
Teacher spread0.328 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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