Jet Lift Bridges Transition Gaps Between Various Forms of Artificial Lift in Horizontal Well Lifecycle
Bibliographic record
Abstract
Abstract This paper aims to share insights from a case history of jet lift applications in the Permian Yeso play. Apache Corporation has been actively drilling horizontally and multistage fracturing the Yeso formation in Eddy County, N.M., targeting dolostone/limestone/sandstone reservoirs interbedded with shale and anhydrite. The Yeso yields oil and liquids-rich gas at depths of 5,000-6,000 feet. Apache's initial strategy was to commence post-flowback production from fractured wells with electrical submersible pumps and then transition to rod lift as rates declined over time. However, as the wells approached the transition window between ESPs and rod pumps, high sand content, wellbore deviation and gas-to-liquids ratios caused frequent downtime for both types of lift, negatively impacting well performance. These conditions caused Apache to experiment with other forms of lift to seek a solution in horizontal wells in the transitionary window. This paper focuses on the trial and success of using concentric jet pumps in place of ESP and rod lift systems. Not all horizontal wells will be ideal candidates for this form of lift. This is especially true when ESP or rod lift can lift the well without issue or with limited downtime due to efficiency differences. However, jet lift is an underutilized form of lift that can produce wells from early life, through the steep horizontal decline, and into the late life steady state decline. This paper aims to show the versatility, under the right circumstances, inherent to downhole jet pumps with an example of a successful installation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".