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Record W2916679523 · doi:10.2118/0214-0104-jpt

Technology Focus: Well Testing (February 2014)

2014· article· en· W2916679523 on OpenAlexaboutno aff
Angel G. Guzman-Garcia

Bibliographic record

VenueJournal of Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityFossil fuelRenewable energyNatural resource economicsPopulationHydraulic fracturingTight gasPetroleum engineeringBusinessEngineeringEconomicsEconomic growthWaste management

Abstract

fetched live from OpenAlex

Technology Focus As throngs of people crowd the car dealerships eager to buy hybrid or electric vehicles to stop using fossil fuels to drive their cars, I reminisce about the good old days when hydrocarbons ruled the world of energy. Oh, wait, that is the start of my upcoming novel! Renewable energy and nuclear power are the world’s fastest-growing energy sources, each increasing 2.5% per year. However, it is estimated that fossil fuels will continue to supply nearly 80% of world energy use through 2040. Natural gas is the fastest-growing fossil fuel, as global supplies of tight gas, shale gas, and coalbed methane increase. Rising prosperity in China and India is a major factor in the outlook for global energy demand. This is great news for our industry because it forces us to continue finding new resources to meet the world’s demands. The massive deepwater reservoirs seem to have been discovered and are, for the most part, in the field-development and production phases. The unconventional reservoirs open new possibilities. Although the term is used indiscriminately for rocks that exhibit permeability values in the nano- to microdarcy range, these unconventional reservoirs fall into various categories that must be exploited differently. Common practice is that massive fractures are required to stimulate hydrocarbon production. But, in many developed countries, the mere mention of the word “fracturing,” or “fracking,” sends shivers down the collective spine of the general population to the point that governing bodies have simply prohibited such practice. Thus, the reservoirs remain unproduced. And they will remain so unless new technologies are developed or the public is eventually educated on the benefits and safety of this procedure. In the meantime, shale gas and coalbed methane, or coal-seam gas, continue to gain acceptance in countries other than the US, where most of the initial techniques have been tested with reasonable success. Interest in exploiting these types of reservoirs has gained momentum in places such as Australia, Argentina, China, Canada, Russia, and even the Middle East. Allow me to say that this is not a comprehensive list; other countries are also opening the doors for companies to find, develop, and produce hydrocarbons from these reservoirs. During the last year, a large number of publications have dealt with testing unconventional reservoirs. Although it was difficult to select three articles from the many manuscripts, I hope that the three chosen convey the interest in these reservoirs and the clever use of well-test data to add knowledge into understanding the producibility of these reservoirs. Finally, let me remind the interested reader that many other articles on this subject are available in the OnePetro library. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 164349 Innovative Single-Phase Tank Technology for In-Situ Sample Validation Enhances Fluid-Sampling Technology by Francisco Galvan Sanchez, Baker Hughes SPE 164482 Inferring Interwell Connectivity in a Reservoir From Bottomhole-Pressure Fluctuations in Hydraulically Fractured Vertical Wells, Horizontal Wells, and Mixed Wellbore Conditions by Anh V. Dinh, Schlumberger, et al. SPE 166074 In-Situ Poisson’s-Ratio Determination From Interference Transient Well Tests by Mojtaba P. Shahri, The University of Tulsa, et al. IPTC 16711 Deepwater Reservoir Characterization Using Tidal Signal Extracted From Permanent Downhole Pressure Gauge by Xingru Wu, University of Oklahoma, et al.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes1
Has abstractyes

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