Produced water treatment and reuse in hydraulic fracturing: Using laboratory research to select and implement technology at field-scale
Bibliographic record
Abstract
Reusing produced water for hydraulic fracturing simultaneously satisfies challenges of fresh water sourcing and the design/operation of an extensive disposal well infrastructure. This presentation provides an overview of a reuse program from concept through implementation including qualification of advanced water recycling technologies. We target the most prolific unconventional reservoir play in the United States – the Permian Basin. Sourcing water for full-field development therein represents a significant problem since it is in short supply in (semi)arid regions of West Texas and Southeastern New Mexico. We report results from a synergistic industry-academia collaboration wherein desalination pretreatment was first evaluated at lab scale to (i) systematically evaluate partial softening (i.e. “floc-and-drop”) versus neutral pH oxidation for iron removal (ii) investigate synergistic effects of FeCl 3 and polymer addition to destabilize colloids (including particulate iron) and induce high-rate sedimentation and (iii) develop and implement robust techniques using video and image analysis to characterize process performance and floc properties (e.g. morphology, size, and settling velocity). Jar tests and associated measurements were completed in the range 4 - 44 ºC covering the range of temperatures measured in the Permian. FeCl 3 in conjunction with an anionic polymer dramatically improved colloid destabilization and floc growth via enmeshment of primary colloids by amorphous iron precipitates and inter-particle bridging by the adsorbed polymer. Larger, stronger, and denser flocs thus formed settled extremely rapidly without breakage (i.e. high rate sedimentation). Bench-scale results were integrated in the design and testing a 5,000 BPD pilot scale high-rate clarifier. Pilot scale results show that the neutral pH method of clean-brine generation produced 5-10 times less sludge while achieving 15-20% higher throughput over the alternative floc-and-drop method. Both bench- and pilot-scale findings were incorporated in design and operation of a 50,000 BPD full-scale reuse facility in the Permian Basin. This presentation will share lessons learned from operating a large-scale reuse facility and how academic research can inform and be motivated by industrial practices (and vice versa).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".