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Record W1988100870 · doi:10.2118/0311-0097-jpt

Relationship Between the Hydraulic Fracture and Observed Microseismicity in the Bossier Sands of Texas

2011· article· en· W1988100870 on OpenAlexaboutno aff
Dennis Denney

Bibliographic record

VenueJournal of Petroleum Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMicroseismGeologyHydraulic fracturingPermeability (electromagnetism)Petroleum engineeringPore water pressureGeotechnical engineeringMining engineeringSeismology

Abstract

fetched live from OpenAlex

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 137936, ’Relationship Between the Hydraulic Fracture and Observed Microseismicity in the Bossier Sands, Texas,’ by A. Guest, SPE, and A. Settari, SPE, University of Calgary, prepared for the 2010 Canadian Unconventional Resources & International Petroleum Conference, Calgary, 19-21 October. The paper has not been peer reviewed. There is an increased effort to improve our understanding of hydrofracturing by use of microseismic monitoring and analysis. A numerical method was developed to predict microseismicity occurring during hydrofracturing and the influence of fracturing on the permeability of the reservoir. The core of this technique is a representative pre-fractured volume of the reservoir that deforms locally and allows the coalescence of deformation as the stress re-equilibrates. Introduction Hydraulic fracturing is used to increase the effective permeability of a producing formation and, thus, increase the production of hydrocarbons. Even though microseismicity provides a reasonable estimate of fracture parameters, our understanding of the relationship between microseismic events and the induced hydraulic fracture is not complete. Hydrofracturing of the Bossier sandstone in east Texas was studied. Early on, hydrofracturing was modeled assuming a single fracture propagating from the treatment well, with geomechanical permeability changes determined from changes of fluid pressure or stress. Reservoir properties were matched to fit the bottomhole pressure for the hydrofracturing and production for an 80-day period such that the permeability used in this model reasonably represents the permeability of the reservoir. A technique was developed to simulate the occurrence of microseismic events during hydrofracturing by numerical modeling of fracture self-localization. This technique forms fractures the size of the grid cell and allows coalescence of neighboring fractures, depending on the evolution of stress in the reservoir. The analysis of the deformation modes (seismic-moment tensors) of such fractures showed that such deformation, even though locally and temporally variable, reflects the overall macroscopic deformation that would be caused by classical hydrofracture propagation. Technique To solve for the deformation associated with hydrofracturing, mechanical equations and equations for fluid motion in porous media must be solved. The equations are coupled through pressure and permeability. In this paper, because the calculation of permeability for this technique is still not fully resolved, the mechanical and fluid-flow solutions are coupled in only one way—by treating the pressure function, generated by the flow model with pressure-dependent permeability and a hydraulic fracture, as input into the mechanical equations. Then, the local 2D geomechanical solution is based on the constitutive and geometrical equations, and mechanical equilibrium equations, which are detailed in the full-length paper. The fracture formation is solved by use of damage theory. The damage initiates when the Mohr-Coulomb or tensile-failure criteria are satisfied locally. The heterogeneity of the material can be introduced through a random function following the Weibull distribution. The heterogeneity is input for strength of the material and Young’s modulus.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.438

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)0.0000.000

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 head0.028
GPT teacher head0.227
Teacher spread0.199 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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