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Record W2077894186 · doi:10.2118/1014-0160-jpt

Technology Focus: Tight Reservoirs (October 2014)

2014· article· en· W2077894186 on OpenAlexaboutno aff
Leonard Kalfayan

Bibliographic record

VenueJournal of Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPaceUnconventional oilCredibilityTight gasLead (geology)Production (economics)Petroleum industryNatural resource economicsFossil fuelPetroleum engineeringRisk analysis (engineering)BusinessGeologyEconomicsHydraulic fracturingEngineeringPolitical sciencePaleontology

Abstract

fetched live from OpenAlex

Technology Focus Exploration and development of unconventional hydrocarbon reservoirs continue to grow at a pace that exceeds our understanding of the nature of these ultratight formations and how to sustain longer-term production from them. This is confounded further by the expansion of the industry into the broader and increasingly mysterious realms of liquid-rich and tight oil reservoirs beyond the relatively better- understood tight gas formations with their longer history of exploitation. With this greater breadth of activity, the unknowns, uncertainties, and challenges are increasing. Already, estimates of unconventional hydrocarbon reserves have varied greatly, and the financial and future-energy-outlook implications of such wide-ranging uncertainty are enormous. With the industry moving forward with exploration and development in the broader categories of unconventional resources globally, it is of paramount importance to assess and improve the different methods and tools for estimating reserves, to establish their accuracy and credibility. And this must be accomplished in conjunction with increasing our fundamental understanding of unconventional formations at the nanoscale. This is a considerable challenge but one with significantly greater emphasis and efforts across the academic and industry research-and- development landscape. In conjunction, there is increasing attention on addressing the immediate issue of rapid well-production declines from the often prolific, but short-lived, initial production rates. With that in mind, methods including the use of enhanced recovery fluids and well-pattern schemes, and reactive fluids such as acids in drilling, completion, and stimulation processes, are beginning to receive genuine consideration. The importance cannot be overstated because alternatives to hydraulic fracturing may become a necessity, at least in certain areas of the world. Through special core analysis and flow studies, visualization techniques, and simulation, acid stimulation in carbonate-rich tight oil formations (as one example) may find greater and more-creative application beyond the acid spearheads ahead of hydraulic-fracturing stages. Other strategies for enhancing production and extending production performance, such as through imbibition and post-stimulation shut-in strategies, especially in liquid-rich and tight oil developments, are receiving more attention and are providing new and important learnings, too. The papers featured this month provide new insights and examples in some of these key, and exciting, areas of current focus in unconventional, tight oil and gas, and shale developments. The reader is encouraged to delve into these topics and continue to monitor progress, which is moving at a fast pace. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 163814 Prediction of SRV and Optimization of Fracturing in Tight Gas and Shale Using a Fully Elastoplastic Coupled Geomechanical Model by M. Nassir, University of Calgary, et al. SPE 167092 Evaluating Treatment Methods for Enhancing Microfracture Conductivity in Tight Formations by Philip D. Nguyen, Halliburton, et al. SPE 167713 Water Loss vs. Soaking Time: Spontaneous Imbibition in Tight Rocks by Q. Lan, University of Alberta, 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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