Field Application of Very High Volume ESP Lift Systems for Shale Gas Fracture Water Supply in Horn River, Canada
Bibliographic record
Abstract
Abstract Unconventional shale gas plays are emerging and undergoing rapid development at an alarming rate across North America. One of the most challenging dynamics of the Horn River, British Columbia, Canada shale play has been the increasing number of fracture stimulations required per horizontal wellbore. Of critical importance is the massive volume of water required to supply the 24/7 fracturing operations. In an effort to eliminate the reliance on surface waters, Encana engaged in a novel approach to produce high volumes of sour waters from a relatively unknown under-pressurized saline aquifer. The challenge was designin7g an appropriate high volume lift system capable of producing and achieving volumes of up to 8,500 m3/d/wellbore while addressing unknown fluid inflows, fluid transmissibility & re-charge, and reservoir fluid chemistry characteristics. Electric submersible pumps (ESPs) have proved to be a successful and reliable artificial lift system for production of high volumes of fluid for many decades. This paper describes the steps undertaken by Encana in designing, piloting and successful field implementation of full scale high volume sour water lift ESP systems. These systems have demonstrated significant potential to alleviate the high costs, production withdrawal limitations and environmental impact of delivering large volumes of fracture supply waters in a remote Northerly region. Modifications to the wellbore completion architecture, equipment design and techniques to optimize pumping operations and water distribution from the field trials are presented.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".