Bibliographic record
Abstract
The Haynesville Shale might be the greatest comeback story of the US shale revolution. At one point in 2016, there were just 13 active rigs in the dry-gas play that stretches between Louisiana and Texas. That figure has increased fourfold to today and, in April, the Haynesville set a new production record of 10.5 Bcf/D—the strongest output since 2011, per the US Department of Energy (DOE). The resurgence has been led not by the large operators who initially put the Haynesville on the map, but by a group of private equity-backed operators that entered the play within the last few years. Their momentum has been driven by liquefied natural gas (LNG) facilities that began exporting the product from the US Gulf Coast in 2016. Petroleum engineers have also moved the needle by creating a class of horizontal wells that look nothing like what the region has seen before in terms of productivity. “I think that over the past 3 or 4 years, the understanding of optimal frac design and the selection of the targets has evolved quite dramatically,” said Artem Abramov, the head of shale research for Rystad Energy in Oslo. The nearly 300 wells that were brought online in the Haynesville during 2017 have produced a cumulative average of 3.6 Bcf. They continue to generate an average of 8.5 MMcf/D, according to public data compiled by ShaleProfile. This is a marked improvement over 2015 vintage wells that managed only 1.5 MMcf/D after reaching the same cumulative production point. In terms of long-tail production, this is an increase of more than 450%. One of the most critical factors behind the improved recovery is the increased use of proppant in horizontal wells. Thanks to lower prices made possible by the in-basin mine trend taking hold across the US shale sector, operators can afford to place two or three times the amount of sand into Haynesville’s tight-rock system than they were prior to 2016. Abramov remarked that this development has effectively “re-established it as a completely new play.” Too Much of a Good Thing Rig counts have remained steady at around an average of 55 since the start of the year but the region is seeing more completion activity and has become one of the fastest-growing demand centers for hydraulic fracturing crews in the US. As the race to reawaken the sleeping giant continues on, there are concerns that too much production could harm the play’s economic standing. Calgary-based RS Energy Group (RSEG) recently looked at the issue and found that takeaway capacity may hit a wall as early as this summer, possibly curtailing drilling plans for the rest of the year. “It’s not a Haynesville constraint in isolation,” explained Trevor Sloan, an executive director and co-head of research at RSEG. “Rather, Haynesville producers share common routes to coastal demand centers—including LNG facilities—with northeast producers, and the midcontinent for that matter. Potential from all those basins could cause some congestion on routes south.”
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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 | 0.001 | 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".