Bibliographic record
Abstract
Beyond the Headlines Editor’s note: Professionals in the oil and gas industry often receive questions about how industry operations affect public health, the environment, and the communities in which they operate. Of particular concern today is the impact of hydraulic fracturing on the environment. In this new column, JPT is inviting energy experts to put those questions and concerns about industry operations into perspective. Additional information about the oil and gas industry, how it affects society, and how to explain industry operations and practices to the general public is available on SPE’s Energy4me website at www. energy4me.org. There are headlines every day that discuss the ethics and safety behind oil and gas operations, particularly hydraulic fracturing. According to the media, hydraulic fracturing can cause earthquakes, contaminated water, and even deformity in animals (if you believe the movie Promise Land). The truth behind the headlines is that hydraulic fracturing is a safe way to get natural gas out of the ground. What makes it a safe practice is solid well construction. The evolution of oil and gas well construction has passed through many frontiers with each new foray into the next “unconventional” hydrocarbon resource generating the needed technology to keep pace with the immediate needs. In light of more than 4 million wells drilled in North America over the past 194 years, it is somewhat surprising that the industry has been successful so many times, and what we have done with lessons learned from the relatively few failures. Wells are designed from the bottom to the top and from the inside outward, but they are drilled and constructed in exactly the opposite manner—often by practitioners with metrics different from the initial design principles. The fundamental objective that must shape every action along the way is that the final product of well construction must be a highly durable pressure vessel, albeit one that is composed of hundreds of threaded connections with a variety of seals and with a long coat of cement. Few other engineering disciplines operate in this highly cloaked area, in which the final engineering product, the downhole section of the well, cannot be conventionally seen, heard or touched, and produces a product that no one really wants to smell or taste. The birth of the US gas industry was ushered in by William Hart’s shale gas well in Fredonia, New York, in 1821. He encountered flowing gas at 28 ft and, consistent with the technology of the time, cased it with wood and flowed shale gas through wood and early steel pipes to light the streets and buildings previously illuminated with lamps filled with whale oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".