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Record W2785299246 · doi:10.3968/10084

Analyzing of Multi Field Coupling Borehole Collapse Time for Shale

2017· article· en· W2785299246 on OpenAlexvenueno aff
Xiqiang Miao

Bibliographic record

VenueAdvances in petroleum exploration and development · 2017
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsnot available
Fundersnot available
KeywordsBoreholeDrilling fluidPore water pressureCohesion (chemistry)GeologyCoupling (piping)Geotechnical engineeringOil shalePermeability (electromagnetism)DrillingStress fieldHydraulic fracturingMechanicsFluid dynamicsPetroleum engineeringFinite element methodEngineeringMechanical engineeringStructural engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

Based on the theory of semi-permeable diaphragm equivalent pore pressure, the fluid-chemistry coupling diffusivity equation of pore pressure is deduced, and then fluid-solid-chemistry coupling model for porous media is founded. Based on theory deduction and laboratory experiment, a new model which represents the relation between the pore pressure and the rock intensive parameters (cohesion stress and angle of internal friction) is founded. A new model which can calculate the collapse time for shale is developed by using the Coulomb-Mohr failure criteria, and then the finite element simulation program is developed by using the software of FEPG. According to the research, the drilling fluid of low activity can inhabit the increasing trend of pore pressure nearby the borehole effectively, and the drilling fluid of low activity can also inhabit the decreasing trend of cohesion stress effectively under the influence of chemical field and seeping field, which favors the borehole stability. The lithological character and drilling fluid performance should be considered in the process of evaluation and forecast of collapse time, and the drilling fluid can be optimized by using this multi field coupling model.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.276
Teacher spread0.253 · 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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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