Improving proppant placement efficiency using the self-generated gas floating technique
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".