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Record W4250950735 · doi:10.2523/77683-ms

What is the Magic of Water in Producing Sand?

2002· article· en· W4250950735 on OpenAlexaffabout
Vaziri Hans, Barree Bob, Yuxing Xiao, Palmer Ian, Mike Kutas

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsExhibitionCitationLibrary scienceMAGIC (telescope)DownloadArt historyComputer scienceWorld Wide WebHistoryPhysics

Abstract

fetched live from OpenAlex

What is the Magic of Water in Producing Sand? Hans Vaziri; Hans Vaziri BP America Inc. Search for other works by this author on: This Site Google Scholar Bob Barree; Bob Barree Barree & Associates Search for other works by this author on: This Site Google Scholar Yuxing Xiao; Yuxing Xiao Dalhousie University Search for other works by this author on: This Site Google Scholar Ian Palmer; Ian Palmer BP America Inc. Search for other works by this author on: This Site Google Scholar Mike Kutas Mike Kutas BP America Inc. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77683-MS https://doi.org/10.2118/77683-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Vaziri, Hans, Barree, Bob, Xiao, Yuxing, Palmer, Ian, and Mike Kutas. "What is the Magic of Water in Producing Sand?." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77683-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 Annual Technical Conference and Exhibition Search Advanced Search AbstractWe performed a comprehensive sand prediction study of several deep, HPHT wells within a large reservoir and the findings revealed that, for these wells, common criteria based on critical drawdown, minimum bottomhole pressure, depletion or fluid velocity failed to predict the outcome by a relatively large margin. All these wells were subjected to relatively high levels of drawdown and also very high fluid velocities and, with the exception of one well, none showed any sanding until water production was encountered.In this paper, we provide a rationale for why water can be highly effective in inducing sand production and we support our argument using advanced numerical modeling. This exercise also ranks the performance of some of the common tools and theories that are conventionally used for sand prediction. We also provide reasons why some of these models do not perform satisfactorily for the cases studied.The originality of the work is in demonstrating that prior to sand production, the dis-aggregated rock (i.e., individual sand particles) around the wellbore is basically held together by the capillary tension which is destroyed by water flow. While the capillary tension appears to be insignificant (as it is in the order of 1 psi or so), it provides a significant resistance against sand mobilization. The importance of this issue has been quantified using advanced numerical modeling. This concept is vastly different from the previous theories that propose water weakens the rock through chemical interaction or changing the relative permeability.IntroductionWhile a great deal of work has been done in the general area of sand production1–15, approaches used to quantify the volumes of the produced sand have faced challenges in the validation process (Class A prediction). In fairness, it is difficult to firmly single out the deficiencies when predictions do not materialize in a consistent fashion as the quality of the input data, monitoring of sanding events as well as the assumptions and physics used for modeling sanding can all be potential culprits. Following up on this line of reasoning, it is not the intention of this paper to prove or refute any previous work done in sand production studies nor to show that our method is universally superior. The primary intention is to provide a deeper insight into the mechanisms of sanding, in general, and water-production induced sanding, in particular. We try to support the views presented using basic fundamentals along with field observations.Significance and Potential ApplicationsConventional sand production models2,3,15–17 predict the onset of sanding which in practice is presumed to signify large-scale sanding. This single case solution scenario does not give operators options to assess risks and benefits which is becoming increasingly more relevant under the currently optimized completion and production practices. In essence, operators would like to know, at any stage in a well's life, how much sand will be produced (rate and duration) for a given production strategy (e.g., maximum drawdown, effects of bean-up and shut-in cycles, impact of water). By better understanding the role of various variables one is enabled to choose the optimal completion method for the life of the well (which may exclude installation or deferring sand control measures) and quantify the impact of aggressive fluid production strategies in terms of volume and rate of sanding. Keywords: stability, capillary tension, reservoir characterization, spe 77683, mechanism, cementation, adhesion, water production, production control, upstream oil & gas Subjects: Well & Reservoir Surveillance and Monitoring, Reservoir Characterization, Perforating, Completion Installation and Operations, Completion Operations This content is only available via PDF. 2002. Society of Petroleum Engineers 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.015
GPT teacher head0.220
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2002
Admission routes2
Has abstractyes

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