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Record W4405940204 · doi:10.1016/j.jgsce.2024.205536

Improving proppant placement efficiency using the self-generated gas floating technique

2025· article· en· W4405940204 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGas Science and Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringComputer scienceEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Hydraulic fracturing plays an essential role in producing unconventional hydrocarbon resources. The success of hydraulic fracturing operations and the performance of developing unconventional hydrocarbon resources strongly depend on the distribution of proppants in the induced fractures. However, due to the gravitational force, proppants tend to settle down rapidly in the induced vertical fractures. This behaviour leads to the closure of the unpropped fracture at the higher sections after the hydraulic fracturing operation and a lower filling efficiency. Many studies on proppants have been made to improve the proppant filling efficiency. However, they are limited to the methods mainly focused on reducing proppants' density or increasing the buoyancy force of fracturing fluid. We desire to examine if gas bubbles can be generated inside fractures to improve proppant placement efficiency. Here, the self-generated gas floating technique is proposed for the first time. In this study, we conduct a series of experiments to demonstrate the feasibility of the self-generated gas floating technique. Firstly, the affinity between air/CO 2 bubbles and proppants is investigated by measuring the contact angles of water/CO 2 bubbles on different types of proppants. Secondly, we conduct experiments under ambient conditions to visualize proppant distribution after implementing self-generated CO 2 bubbles inside the fracture model. Lastly, we conduct the high-pressure experiment to verify whether the self-generated gas floating technique can lift proppants under high-pressure conditions. Experimental results indicate that resin-coated 30/50 mesh-sized proppants have demonstrated a strong adhesion force to CO 2 bubbles. The self-generated CO 2 bubbles add an external lifting force to the proppants, bring the proppants to a higher fracture location, and increase the proppant filling efficiency. Another significant observation of the self-generated gas floating technique under high-pressure conditions is that a steep pressure decline during the completion stage can lead to a spontaneous generation of CO 2 bubbles inside the fracture. • Self-generated gas floating technique is proposed to increase proppant placement efficiency. • The proposed technique works better for resin-coated proppants than ceramic proppants. • A larger pressure decline rate is more beneficial for applying the self-generated gas floating technique.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.497
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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