MétaCan
Menu
Back to cohort
Record W4251895859 · doi:10.2523/65510-ms

Sand Production Prediction for Horizontal Wells in Gas Storage Reservoirs

2000· article· en· W4251895859 on OpenAlexaboutno aff
P.J. McLellan, C.D. Hawkes, R.S. Read

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringProduction (economics)GeologyEnvironmental sciencePetrologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Sand Production Prediction for Horizontal Wells in Gas Storage Reservoirs P.J. McLellan; P.J. McLellan Advanced Geotechnology Inc. Search for other works by this author on: This Site Google Scholar C.D. Hawkes; C.D. Hawkes Advanced Geotechnology Inc. Search for other works by this author on: This Site Google Scholar R.S. Read R.S. Read Advanced Geotechnology Inc. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. Paper Number: SPE-65510-MS https://doi.org/10.2118/65510-MS Published: November 06 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation McLellan, P.J., Hawkes, C.D., and R.S. Read. "Sand Production Prediction for Horizontal Wells in Gas Storage Reservoirs." Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. doi: https://doi.org/10.2118/65510-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE/CIM International Conference on Horizontal Well Technology Search Advanced Search AbstractSand production and the sand control technique selected to mitigate or eliminate it can have a critical influence on the performance of horizontal wells in gas storage reservoirs. A completion that provides adequate sand control is usually required, but an overly conservative completion design can have an unnecessary, negative consequence on gas well productivity and injectivity. The selection of the appropriate sand control design depends on the characteristics of the reservoir formation, the in-situ stress state, the maximum and minimum values of the reservoir pressure during gas storage operations, the drawdown pressure, the near-well fluid saturations, the well trajectory and the capacity for handling sand in the well tubulars and surface facilities.This paper reviews the principal causes of sand production from borehole and perforation collapse, and demonstrates the application of commercial software programs for assessing sand production risks. The influence of formation damage, compressible and non-Darcy gas flow effects, reservoir pressure changes and rock strength reduction due to cyclic loading will be illustrated. Additional risks, such as exceeding the fracture breakdown pressure in the reservoir during injection, or collapsing weak, interbedded shale strata that are locally penetrated by the horizontal well, are also described. Numerical geomechanical modelling techniques suitable for more complex material behaviour and fluid flow phenomena are also described, as well as a novel procedure for estimating initial produced sand volumes.Several field examples are presented, illustrating results for reservoirs ranging from relatively strong rocks, in which no sand control is required, to poorly cemented sandstones which require gravel-packed or screened completions.IntroductionSand Production Prediction.A number of methods for assessing sand production risks using geomechanical models have been described previously. An effective approach that has emerged from research in this area is the use of elastoplastic models, which can predict the extent of rock yielding around a borehole or a perforation. For example, Bratli and Risnes1 and Risnes et al.2 developed analytical solutions for rock yielding around perforations and boreholes, respectively, for steady-state, incompressible fluid flow into a wellbore in an elastic perfectly-plastic material. Wang and Dusseault3 and McLellan and Wang4 developed a model for assessing rock yielding around a borehole in an elastic-brittle-plastic material that undergoes instantaneous strain softening. This model was also based on steady-state, incompressible fluid flow conditions, but included the capability to model the effects of rock yielding on permeability. The latter effect has been shown to have a significant effect on near-well pressure gradients, and hence on rock yielding and sand production risks, for certain classes of problems. Weingarten and Perkins5 developed a rock yielding model for perforations in elastic-perfectly plastic materials, including the effects of steady-state, compressible fluid flow behaviour. Wang and Peden6 and Ong et al.7 developed models for assessing sand production risks for perforations based on shear yielding and tensile failure criteria, respectively, which also included the effects of non-Darcy flow on near-well pressure gradients. Detournay and Fairhurst8 and Bradford et al.9 developed rock yielding models for predicting non-circular yielded zones around boreholes or cylindrical perforations subjected to non-hydrostatic in-situ stresses. McLellan and Hawkes10 developed a method for implementing a rock yielding model for elastic-brittle-plastic materials subjected to non-hydrostatic in-situ stresses within the framework of a probabilistic simulation that accounts for uncertainty and spatial variability of key input parameters such as in-situ stress magnitudes and rock mechanical properties.Sand Production Prediction.A number of methods for assessing sand production risks using geomechanical models have been described previously. An effective approach that has emerged from research in this area is the use of elastoplastic models, which can predict the extent of rock yielding around a borehole or a perforation. For example, Bratli and Risnes1 and Risnes et al.2 developed analytical solutions for rock yielding around perforations and boreholes, respectively, for steady-state, incompressible fluid flow into a wellbore in an elastic perfectly-plastic material. Wang and Dusseault3 and McLellan and Wang4 developed a model for assessing rock yielding around a borehole in an elastic-brittle-plastic material that undergoes instantaneous strain softening. This model was also based on steady-state, incompressible fluid flow conditions, but included the capability to model the effects of rock yielding on permeability. The latter effect has been shown to have a significant effect on near-well pressure gradients, and hence on rock yielding and sand production risks, for certain classes of problems. Weingarten and Perkins5 developed a rock yielding model for perforations in elastic-perfectly plastic materials, including the effects of steady-state, compressible fluid flow behaviour. Wang and Peden6 and Ong et al.7 developed models for assessing sand production risks for perforations based on shear yielding and tensile failure criteria, respectively, which also included the effects of non-Darcy flow on near-well pressure gradients. Detournay and Fairhurst8 and Bradford et al.9 developed rock yielding models for predicting non-circular yielded zones around boreholes or cylindrical perforations subjected to non-hydrostatic in-situ stresses. McLellan and Hawkes10 developed a method for implementing a rock yielding model for elastic-brittle-plastic materials subjected to non-hydrostatic in-situ stresses within the framework of a probabilistic simulation that accounts for uncertainty and spatial variability of key input parameters such as in-situ stress magnitudes and rock mechanical properties. Keywords: horizontal well, sand production prediction, cohesion, reservoir characterization, production risk, peak cohesion, detachment, sand production risk, reservoir geomechanics, drawdown pressure Subjects: Reservoir Characterization, Reservoir geomechanics This content is only available via PDF. 2000. SPE/PS-CIM International Conference on Horizontal Well Technology You can access this article if you purchase or spend a download.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations8
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207