A Few Lessons From Tight-Rock Producers That Have Made Big Investments in Fracture Diagnostics
Bibliographic record
Abstract
_ Devon Energy may have unlocked why hydraulic fractures in some tight-rock plays grow faster than others. This appears to be strongly linked to the well-density potential of these plays. The operator is also working on a new way to measure how fractures squeeze steel pipe, which, in turn, might result in more-efficient completion designs. SM Energy recently studied the interactions between hydraulic fractures and faults in one of its tight-rock projects in Texas. When a fault slipped, the Denver-based producer had seen production slip too. Now, armed with enough data, SM Energy has adopted new completion designs that avoid overpressurizing the faults found all over the target formation. Shell’s search for light-tight oil in the Permian Basin ended about a year ago but what the company learned there will live on in its other unconventional projects. Not least of those learnings involves how to best deploy diagnostic technologies to answer big questions about the way fractures behave—no matter the formation. These glimpses into how each unconventional operator is relying on fracture diagnostics were shared at a recent technical conference organized by Calgary-based SAGA Wisdom which offers training for engineers on reservoir analysis. Here is more on the lessons they shared with the industry at the conference held this year in Fort Worth, Texas. Pick the Right Tools Som Mondal, an engineer with Shell’s shale business unit, joked that when he agreed to speak at the conference about the company’s diagnostics journey in the Permian, it still owned assets there. The London-based supermajor exited the largest US onshore play in a $9.5-billion sale to ConocoPhillips that closed in December 2021. But while it has moved on from the Permian, Shell still operates unconventional assets in Argentina’s Vaca Muerta and the Montney Shale in Alberta. It is in those places where the company’s years of integrated diagnostics research in the Permian will be carried forward. Mondal shared how that work shaped the way he now views the different diagnostics technologies and where they complement each other. On this, he subscribes to two overarching philosophies. “One, we need to aim for consilience—which is when different, independent approaches converge toward the same solution,” said Mondal. “And second, we need to weigh the different diagnostics so that we can combine them based on our confidence in them and the scope of its measurement.”
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".