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Record W4254455584 · doi:10.2118/100576-ms

Well Testing of Tight Gas Reservoirs

2006· article· en· W4254455584 on OpenAlexaff
José Pérez García, M. Pooladi-Darvish, Frank Brunner, M. Santo, Louis Mattar

Bibliographic record

VenueSPE Gas Technology Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTight gasPetroleum engineeringPermeability (electromagnetism)InflowFossil fuelGeologyHydrostatic testNatural gas fieldEnvironmental scienceHydraulic fracturingNatural gasEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Advances in technology, strong energy prices and declining reserves in conventional gas reservoirs are encouraging oil and gas companies to consider the feasibility of exploiting large reserves trapped in tight (low permeability) gas reservoirs. Conventional well tests conducted on these low permeability gas formations, generally result in poor estimates of key reservoir parameters such as: initial reservoir pressure, permeability, effective fracture length, fracture conductivity, and deliverability potential. The objective of this paper is to review the different types of tests that are particularly applicable to tight gas formations, discuss why the traditional methods of testing and analysis rarely succeed, and identify appropriate test and analysis procedures for tight gas reservoirs. We will consider short-term tests where the primary objective is to obtain the initial reservoir pressure, with a secondary objective of determining permeability and skin. Perforation Inflow Tests, Fracture-Calibration Tests, and Formation Flow Tests will be considered. The applicability of these tests will be shown using synthetic and actual field cases.

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 score0.677
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.192
Teacher spread0.187 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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