Bibliographic record
Abstract
Apache found something positive to say about its huge gas-producing play in the Permian at a time when gas is selling at rock-bottom prices. When it shut in a 14-well pad on its Alpine High play for 60 days, gas and condensate production surged. It was a rare test of whether a production break can allow water in rock near the fracture face to soak in deeper, allowing gas and liquids to flow more freely. “The gas rate came back above the pre-soak rate and it’s actually holding in pretty flat, so it shows there was some impact and the condensate rate came in higher than the pre-soak rate,” said Dave Pursell, executive vice president of planning, reserves and fundamentals, during the company’s third quarter earnings call. The wells were on the huge Blackfoot pad, which Apache had highlighted a year ago as a major test of the industry trend toward concentrating many wells at a single location to reduce development costs. In mid-2018, the company said the pad was producing 99 mm ft3/d, and 200 B/D of oil from 12 wells. Based on the comment during the call, production since then has been disappointing. “We thought, let’s take advantage of low commodity prices and initiate a 60-day soak to really understand whether it is a relative permeability issue or what is the mechanism for the underperformance,” Pursell said. The price of doing the test was a significant decline in Alpine High production—Apache said the shut-in was a prime reason for a 30% drop in production there. But with gas prices so low, the expected upside apparently justifies the revenue delayed. During that earnings call, Apache also said it would reduce the number of rigs working in the Alpine High play from five to two so it could concentrate on Permian Basin acreage. Stories about the benefits of soaking have been around for a long time. As has talks about its downsides. In those cases, production was not an option, often due to lack of pipeline access or processing facilities. Apache tried soaking on the big pad because, “a significant amount of water was pumped into a small area of the reservoir, which may have impacted well productivity,” Pursell said. To answer that question and others, Pursell said Apache has “a team of folks working on the Blackfoot and all the multiwell pads we drilled and completed to date.” Is It Worth It? Apache is getting in the middle of an argument over whether soaking is worth the production days lost. While it often does cause a production spurt, the gains can quickly fade and skeptics argue that soaking can cause clay-rich rocks to swell, causing damage. Robert Hawkes, general manager at Abra in Canada and an SPE Distinguished Lecturer, has long advocated soaking. He said the industry needs to realize that water’s impact goes beyond fracturing rock and delivering sand to prop fractures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".