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Record W4390475865 · doi:10.2118/0124-0026-jpt

25 Years of Technology as Reported in <i>JPT</i>

2024· article· en· W4390475865 on OpenAlexaboutno aff
Stephen Rassenfoss

Bibliographic record

VenueJournal of Petroleum Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBoomHydraulic fracturingNoticeFrontierDrillingPetroleum industryEngineeringPetroleum engineeringMechanical engineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

The past 25 years marked the emergence of shale as the next frontier for oil and gas production, and JPT’s Houston office was right in the middle of it all. Credit for starting this disruptive change goes to George P. Mitchell who led a 17-year-long effort to extract gas from the ultratight rock in the Barnett formation. A JPT story described how this effort was finally looking like a significant, profitable business in 1998 when Mitchell Energy’s engineers turned to lower-cost slickwater fracturing in their vertical wells. The success of this trial-and-error engineering marathon drew little notice at the time. But that seed grew into a huge business based on technology that showed it was possible to profitably produce oil and gas from virtually impermeable rock using large-scale fracturing and ever-longer horizontal wells. In the coming decades this reverberated through drilling, fracturing, and reservoir engineering, disciplines whose physics-based view of the world could not predict how that would happen. As JPT’s staff grew with the addition of reporters covering emerging technology, there was no ignoring what was going on around us. The magazine highlighted futuristic reservoir monitoring ideas in Saudi Arabia and advances in deepwater technology in Brazil, but the booms and busts of this high-profile business that delivered more oil than profits kept changing in interesting ways. The first SPE Hydraulic Fracturing Technology Conference I covered in 2011 was amazing and puzzling to this technology novice. I wondered how they could create fractured areas that looked like shoe boxes. At an early show, a speaker advised those who believed that fracturing knowledge began with shale to look back further in OnePetro. One of those paper authors, then and now, is Dave Cramer, senior engineering fellow for ConocoPhillips, who began learning fracturing when he graduated from college with a business administration degree and got a job with Western Company of North America. The singular change over the past 25 years is how much fracturing has been scaled up. “It like a factory out there, and it was not a factory when we started,” he said. In the previous 25 years, fracturing experts were doing a lot of innovative things. He proudly remembers being involved in a job for Amoco where they pumped 600,000 gallons of gelled fluids and 1.3 million pounds of large-mesh sand into a 35-ft tight sandstone section of a well in the DJ Basin in Colorado. The morning he told that story he was evaluating a well completed in Canada where ConocoPhillips used slickwater to pump 970,000 pounds of sand per frac stage. In some ways the job in 1978 looms larger in his mind. The whole job was aimed at a single target and was sophisticated: They pumped five different fluid mixtures as the job progressed based on their lab testing and fracture modeling. While he thinks the technical skills of those fracturing tight rock back then are underestimated, wells now with laterals that are miles long and hundreds of clusters to stimulate are so much more complex, which has created an insatiable appetite for data by engineers continually feeling pressure for greater production and efficiency.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.272
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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