Effectively Controlling Proppant Flowback to Maximize Well Production: Lessons Learned from Argentina
Bibliographic record
Abstract
Abstract Flowback of proppant and formation sand often poses serious challenges to operating companies. These solids can cause equipment damage, costly and frequent cleanup treatments, and production decreases. Various mechanisms were found that destabilize the proppant pack, causing the proppant to produce back with the production fluid. Since 2005, curable resin systems for coating proppant on-the-fly during hydraulic fracturing completions and remedial proppant treatments of propped fractures have been applied in Argentina to provide an effective means for proppant flowback control and screenless completions in various basins. Evaluation of these applications has helped determine that optimum concentrations of resin coatings on the proppant in either primary or remedial treatments are necessary to maximize the bonding between proppant grains to lock the grains in place while minimizing any reduction of the proppant pack conductivity. Additives included in the liquid resin systems permit good consolidation properties in the proppant pack, allowing it to effectively handle the shear forces of high production rates and multiphase flow and the effect of stress cycling as the well undergoes producing and being shut in. Field results indicate that on-the-fly resin coating on proppant and remedial treatments effectively stops the proppant from producing back while allowing the well production rates to be maximized as designed. These processes have drastically decreased the number of solids cleanout workovers in the treated wells compared to the offset wells in the same field where resin treatments were not performed. These resin treatments provide a reliable and cost-effective alternative in marginal reservoirs, eliminating the need for sand screens and providing access to other intervals when deemed necessary without wellbore restrictions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".