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Record W2987700810 · doi:10.2118/198693-ms

Geomechanical Simulations to Design Well Integrity

2019· article· en· W2987700810 on OpenAlexaff
Yanguang Yuan, Thomas J. Boone

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsBitCan (Canada)
Fundersnot available
KeywordsCasingWorkflowComputer scienceContext (archaeology)Petroleum engineeringGeologyIntegrity management

Abstract

fetched live from OpenAlex

Abstract Rock deformation and fracturing is an important causal mechanism that can compromise well integrity. Geomechanical simulation is a valuable tool to investigate this mechanism and connect well tubular designs with reservoir development strategies. Utilizing relevant field examples, this paper describes a work flow in these regards. Two example simulation approaches are described. One is to use a composite casing/cement/rock model in a reservoir of complex geology to compute maximum strain, dogleg severity, and ovality/restriction in the casing along the well trajectory. Different well design parameters, such as casing size, grade, and cement thickness, can be iterated against different reservoir production strategies. All these efforts are to arrive at an optimized design. The other approach is to calculate localized shear displacement along a weak plane that will be imposed on well tubulars during reservoir activities. The resulting design is optimized by altering well placement and stimulation/production schedules. The above workflow has been proven in various field applications. Experience is shared in this paper. It is hoped this work can demonstrate that the optimal management of well integrity can be achieved by an integrated approach that designs appropriate tubulars and adjusts reservoir activities. Placing well tubulars in the context of rock deformation, geomechanical simulation is the best tool to connect the reservoir activities with the well tubular designs and therefore, can potentially offer a cost-effective well integrity management program.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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.

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
Published2019
Admission routes1
Has abstractyes

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