Silica Gel Fracturing Fluids an Alternative to Guar Systems that Allows the use of West Texas Produced Water
Bibliographic record
Abstract
Abstract The use of freshwater, near freshwater, or treated water in hydraulic fracturing represents an ever-increasing cost in the Permian Basin. Environmental concerns add to the pressure to develop methods to use significantly higher volumes of produced water in hydraulic fracture fluids. To solve the challenge of viscosifying untreated, high total dissolved solids water a move was made away from organic-based viscosifiers to silica-based technology. Fumed silica is highly effective as a viscosifier for high-density brines that has demonstrated excellent low-end rheology, exceptional suspending ability, and a nominal filter cake. However, the high cost of fumed silica and operational challenges have precluded commercial adoption. This paper describes thatsimilar rheology is achievable at a fraction of the cost using a silica gel. The focus of the paper is on the field trials in West Texas where untreated produced water was viscosified with silica gel and run as alternatives to a standard 20 lb/Mgal crosslinked guar fluid made with fresh water. Low cost and operational efficiencies were obtained bypreparingthe silica gel on-location using standard and readily available hydraulic fracturing equipment. Procedures for making the silica gel-based frac fluid were similar to those of making a crosslinked guar fluid. Field trials have demonstrated that silica-gel carries high loadings of 20/40 mesh sand even at low pump rates. Production data from the trials has varied from exceeding expectations to being similar to existing production results.On a chemical cost basis, silica gel is comparable to a borate-cross-linked guar frac fluid. The economics tip very much in favor of silica gel when factoring in the savings using untreated produced water.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".