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Record W2093635120 · doi:10.2118/06-07-05

Non-Destructive Techniques to Determine the Effective Stress Coefficient of Sandstone Formations

2006· article· en· W2093635120 on OpenAlexafffund
Paul K. Frempong, Stephen Butt

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuartzSaturation (graph theory)Effective stressPorosityMineralogyGeologyStress (linguistics)Overburden pressureComputationQuality (philosophy)Geotechnical engineeringPore water pressureAttenuationWork (physics)MechanicsMathematicsThermodynamicsPhysicsOpticsAlgorithm

Abstract

fetched live from OpenAlex

Abstract The concept of effective stress coefficient, "n," is critical for the study of stress and pressure-dependent behaviour of rocks. This parameter is used to scale the pore fluid pressure for the computation of effective stress and impacts on the accuracy of pore pressure and saturation predictions in reservoirs. However, "n" is relatively unknown and difficult to estimate and, in many cases, is often incorrectly assumed to be equal to one. We present a general review of "n" and give a theoretical proof that "n" is less than one for porous rocks. We also introduce a laboratory methodology to measure the value of "n" employing ultrasonic P-wave velocity (Vp) and attenuation quality factor (Qp). We then confirm the value of "n' experimentally by using this laboratory method to estimate "n" of quartz sandstone. We compare our experimental value of "n" to both theoretical and practical results obtained from other literature that used different estimation methodology. We argue from our experimental results that:"n" is not one but varies from zero to one;there is no single value of "n" for a particular reservoir rock;"n" depends on internal factors (porosity and pore geometry) and external factors (pore pressure and confining pressure) of the rock;velocity derived "n" and quality factor derived "n" are slightly different for the same rock; and,for the quartz sandstone used in this experiment, the velocity derived "n" is smaller and also less sensitive to changes in pressure than quality factor derived "n." Introduction Most of the pioneering work on what is now known as effective stress coefficient was done by Terzaghi(1), who showed that most rocks are porous to some degree due to the pore spaces and micro cracks within them. The effect of these pores on the strength of rocks has widely been documented(1–5). A rock with a few or without pores is considerably stronger than porous ones since pore spaces are unsupported within the rock. With the presence of unsupported spaces, the intact rock between the pores has to take larger loads to sustain an overall applied stress. This important phenomenon has a direct bearing on the effective stress of the rock (Figure 1). Thus, the effective stress (Pe) is not directly equal to the differential pressure (Pd). Many writers(4–12) have highlighted the problem caused by this phenomenon and the resulting uncertainties associated with the estimated pressures and saturation in reservoirs. This uncertainty is increasingly significant for reservoir engineering due to the expanding use of seismic techniques for remote estimation of abnormal pore pressures for drill planning and for monitoring reservoir pore pressure changes during production. For example, Carcione and Helle(13) applied a new technique for estimating reservoir pressures from seismic data to an overpressured gas field in the Norwegian North Sea and concluded that once a reliable velocity field was determined for the reservoir formation, the most important part of the prediction process was the determination of the effective stress coefficients and related dry-rock moduli.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.177
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2006
Admission routes2
Has abstractyes

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