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Record W4236080844 · doi:10.2118/2006-195

Effect of Sand Production on Casing Integrity

2006· article· en· W4236080844 on OpenAlexaff
X.Q. Li, T.G. Zhu, L.T. Fang, Y. Yuan

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsBitCan (Canada)
Fundersnot available
KeywordsCasingProduction (economics)Petroleum engineeringEnvironmental scienceComputer scienceGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract This paper documents an engineering study on sand production and its aftermaths on casing damage. Many wells were converted from production to water injection to maintain reservoir pressure and dispose waste water. Formation of precipitates across the injection interval required frequent washing. Inadvertently, the well washing involved reducing the well pressure rapidly. Consequently, solids were released from the formation into the wells. The well washing and associated solid production went unnoticed for an unknown long time until significant casing deformation encountered during a recent working-over. The significant solid production caused casing buckling near the perforation interval. It also activated a weak plane in the overburden, causing further casing damage. This paper will present relevant field data and engineering analyses to support the above conclusions. Field measures to improve the casing's resistance against the buckling are also described. Introduction In the field, a casing is generally situated in a complicated geological environment. It is exposed to formation fluid that can be corrosive. It is mechanically loaded by in-situ stresses, fluid pressure, reservoir compaction or expansion. Along its path, it may traverse many complex geological structures such as faults, thin weak layers, weak bedding planes, weak 3-D bodies (salts), weak-strong interbeds, etc. The downhole temperature can be very high. Besides the natural factors as described in the above, casing integrity may be compromised by human factors. For example, casing failure may have its origin right from the very beginning of a petroleum exploitation program. Inability to drill and maintain a gauged and stable borehole, a poor selection of casing hardware, a poor cement placement, an inappropriate perforation and a non-optimized well placement can all impact the casing integrity. This is further compounded by non-optimized reservoir stimulation/production strategies during the production life span. The complex factors or processes described in the above, being it human or natural, can interact with each other. For example, chemical corrosion may create microcracks in the tubing. The microcracking process will accelerate upon stress loading. As another example, a deformed casing becomes oval. An ovalized casing causes stress concentration which makes the conventional design based on circular geometry invalid. Therefore, casing integrity design is an extremely complex issue. Nevertheless, from a practical point of view, it is always better to design or manage the complexities or uncertainties to prevent casing failures than to remedy a failed casing. This philosophy has repeatedly proven cost-effective in the worldwide industrial practices. Optimum casing integrity can be designed and/or managed if sufficient engineering conscience is executed. The appropriate engineering conscience starts with characterizing the reservoir including its mechanical and eservoir engineering properties. It must also attend the production strategies so that the casing integrity is maintained in accordance with considerations for the production targets. When casing damage happens in the field history, it is critical to analyze the circumstances around the damage and draw lessons from them. All these tasks are executed in this paper. Various fields in our assets experienced casing damage. Plan for increasing the production will accelerate the casing impairment trend.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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 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
Published2006
Admission routes1
Has abstractyes

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