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Record W1983910656 · doi:10.2118/00-05-04

Analysis of Hydraulic Fracturing of High Permeability Gas Wells to Reduce Non-Darcy Skin Effects

2000· article· en· W1983910656 on OpenAlexfundno aff
A. Settari, J.R. Jones, A.J. Stark

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsPermeability (electromagnetism)Darcy's lawHydraulic fracturingPetroleum engineeringGeologyGeotechnical engineeringReservoir simulationPorous mediumMechanicsPorosityChemistry

Abstract

fetched live from OpenAlex

Abstract Hydraulic fracturing has become increasingly popular in high permeability gas reservoirs in order to reduce apparent skin and thus improve well productivity. The remaining post fracture rate dependent skin effect varies from case to case and it is unclear whether this is a result of non-Darcy flow in the fracture, the reservoir or both. This paper presents a study of the effects of reservoir and fracture turbulence in fractured gas wells. A quantification of the fracture length required to eliminate the effects of reservoir turbulence is obtained by means of a numerical study. A similar study with non-Darcy flow in both the reservoir and the fracture results in a correlation of fracture length required to get zero apparent skin at 75﹪ of AOF as a function of reservoir permeability, pressure, fracture conductivity and β factor. Introduction As shown first by Forschheimer(1) flow through porous media deviates from the linear Darcy's law and can be described by a quadratic equation with a non-Darcy flow coefficient β in the nonlinear term. The coefficient β has been correlated to reservoir permeability by numerous authors; in general β decreases with increasing reservoir permeability. Experience has shown that the non-Darcy term becomes more significant in higher permeability gas reservoirs. Generally for reservoir permeabilities below 1 mD there is little effect from non-Darcy flow. In higher permeability reservoirs non-Darcy flow can significantly restrict well production rates and may also affect the pressure transient response. Hydraulic fracturing has become increasingly popular in high permeability gas reservoirs. Normally the goal for hydraulic fracturing high permeability gas wells is to bypass skin damage and thus increase initial flow rates. These 'skin' fractures are generally small in nature and may or may not include high-grade proppants. Prior to performing such a treatment there is often evidence to support a high permeability reservoir and a high apparent positive skin. Unfortunately there is rarely enough data to distinguish if this skin is a true mechanical skin or rate dependent (non-Darcy) skin. Post fracture analysis may show apparent skins ranging from slightly negative to neutral to positive. If a multi-rate post fracture test was performed it is sometimes possible to separate mechanical skin from rate dependent skin. The basic problem with applying the rate dependent skin analysis to a fractured well is that the rate dependent skin was developed based on the assumption of radial homogeneous flow into the wellbore. By adding a hydraulic fracture the flow regime has been altered and the theory breaks down. It is unclear whether the rate dependent skin is occurring in the linear fracture flow or in the reservoir or in both. The primary purpose of this study is to quantify when reservoir turbulence is insignificant for a fractured well. The second purpose is to provide a simple method for predicting post fracture rates in high permeability reservoirs taking into account fracture turbulence. Literature Review The previous work on non-Darcy flow can be divided into two main categories:what is the exact nature of non-Darcy flow andhow to predict when non-Darcy flow will have a significant effect on production.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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

Citations30
Published2000
Admission routes1
Has abstractyes

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