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Record W2772828096 · doi:10.2118/189445-pa

Computed-Tomography Measurements of Water Block in Low-Permeability Rocks: Scaling and Remedying Production Impairment

2017· article· en· W2772828096 on OpenAlexfundno aff
Tianbo Liang, Xiao Luo, Quoc P. Nguyen, David A. DiCarlo

Bibliographic record

VenueSPE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersCMG Reservoir Simulation FoundationUniversity of Texas at Austin
KeywordsImbibitionPermeability (electromagnetism)GeologyScalingCapillary pressurePetroleum engineeringRelative permeabilitySaturation (graph theory)Fluid dynamicsPressure dropPetrologyGeotechnical engineeringMechanicsPorosityPorous mediumChemistryGeometry

Abstract

fetched live from OpenAlex

Summary Fracturing-fluid invasion into the rock matrix can generate water block that potentially reduces hydrocarbon production, especially in low-permeability reservoirs. Here, we experimentally investigate the dynamics of water block under different flow scenarios (i.e., without shut-ins) and rock permeabilities through multiple coreflood experiments. A computed-tomography (CT) scanner is used to obtain the saturation profile within the core throughout the experiment, while the overall hydrocarbon productivity is measured from the overall pressure drop across the core. On the basis of the saturation and pressure measurements, we interpret the potential physical mechanism regarding the productivity reduction from water block and its self-mitigation facilitated by the capillary imbibition. Our interpretation also matches the observed scaling with rock permeability and the optimal shut-in time.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.233
Teacher spread0.217 · 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 designObservational
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

Citations55
Published2017
Admission routes1
Has abstractyes

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