Bibliographic record
Abstract
Fracturing Noble Energy has had powerful motivation to invest in learning how to maximize the return on its wells in the Wattenberg field in Colorado—its biggest current investment and key to the company’s aggressive growth program. Over the past 3 years, the independent oil company has drilled nearly 600 wells in extremely tight Niobrara formation rock. The company plans to go from 320 wells per year in 2014 to 680 wells in 2018, according to a recent Noble Energy investor presentation. This effort is needed as it works to double its daily production over the next 5 years. For the team working to maximize production and profits in that field, one of the biggest questions has been, and continues to be, how many wells to drill per square mile. This is a common question in the industry, but the company has done something uncommon to help answer it: Noble created an “Underground Laboratory.” It is in a section of land with nine horizontal wells, spaced at varying intervals. The “Laboratory” includes a pair of horizontal wells with multiple fiber-optic and pressure sensors amid a cluster of vertical monitoring wells and provides a multidisciplinary team of geoscientists and engineers a rare and unusually detailed look at oil production in the Niobrara on a meter-by-meter basis. “What we are trying to do is essentially bring the equivalent of high-definition television down underground to directly observe what is happening and then come back and figure out, how do we explain what we see,” said Dave Koskella, exploration and reservoir systems manager at Noble. What looks like a geoscientist’s fantasy come true—a list of diagnostic tools included in a presentation at February’s SPE Hydraulic Fracturing Technology Conference in The Woodlands, Texas, consisting of 13 items, some with subheads—was created to answer critical questions facing the business. “It is a laboratory that is challenging our insights and beliefs about why and how things are working in our wells,” he said. While Noble’s investment in diagnostics is exceptional, the problems it is trying to solve are common for the companies focusing their exploration and production program on developing unconventional formations in the United States and Canada.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".