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Record W4392682638 · doi:10.2118/218047-ms

Scaled Physical Modeling of Cyclic CO2 Injection in Unconsolidated Heavy Oil Reservoirs Using Geotechnical Centrifuge and Additive Manufacturing Technologies

2024· article· en· W4392682638 on OpenAlexaff
Daniel Cartagena Perez, Alireza Rangriz Shokri, Gonzalo Zambrano-Narváez, Dymtro Pantov, Yazhao Wang, Rick Chalaturnyk, Chris Hawkes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsCentrifugePetroleum engineeringGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The success of CO2 injection in shallow reservoirs such as CHOPS (Cold Heavy Oil Production with Sands) entails an understanding of the complex interplay of mechanics of gaseous solvent interaction, reservoir deformation, and borehole collapse. This paper provides the design of a highly-instrumented scaled laboratory experiment inside a 2-meter radius beam geotechnical centrifuge, capable of simulating the cyclic CO2 injection into an unconsolidated sandstone specimen at reservoir conditions. Given that sand production during CHOPS creates high-permeability channel-like structures, additive manufacturing technology (i.e. 3D printing with actual sand particles) was used to fabricate the physical model specimen of a scaled reservoir. A centrifuge cell was designed and constructed to simulate the multi-phase cyclic CO2 injection process at the reservoir scale. Scaling factors for stress, time, height and density were used to determine the centrifuge operation. A loading system was included in the centrifuge cell to emulate the vertical stress from the overburden rocks at the top of a shale caprock layer. Stress anisotropy in horizontal stresses were applied through an 8-arm horizontal loading system, similar to a true triaxial cell. A sand trap and a production unit were used to collect the collapsed sands and the produced fluids. To establish residual water saturation, the reservoir prototype was first saturated with water, followed by dead canola oil and live oil (prepared by dissolving CO2). The experiment was started by spinning the 500 kg setup inside the geotechnical centrifuge until it reached a steady rotational speed of 120 revolutions per minute (equivalent to 30 times the gravitational acceleration). The perforations of a scaled wellbore within the reservoir prototype were opened to initiate fluid and sand production. It appeared that the increase in the cohesion of the 3D printed rock in regions away from wellbore reduced rock failure during production cycles even with large seepage forces and pressure gradients. The structural changes around high-permeability zone and near-wellbore region were related to stress concentration. The result of our scaled physical experiments delivers insights on fluid displacement and rock deformation during CO2 saturated oil production from a reservoir prototype. The application of a geotechnical centrifuge and additive manufacturing technology provides a platform to experimentally explore the multi-scale, multi-physics processes of sampling, flow, and deformation issues observed in subsurface systems including H2 storage and safe disposal of radioactive waste.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.226
Teacher spread0.215 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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