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Record W2583844069 · doi:10.2118/1016-0073-jpt

Technology Focus: Sand Management and Sand Control

2016· article· en· W2583844069 on OpenAlexaboutno aff
R. J. Wetzel

Bibliographic record

VenueJournal of Petroleum Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSuspectSoftware deploymentSet (abstract data type)Term (time)Risk analysis (engineering)Control (management)Quality (philosophy)Computer sciencesortOperations researchWork (physics)Focus (optics)Operations managementBusinessEngineeringPolitical scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

Technology Focus I suppose that many of us are taking a deep breath just now. Many of us could be revisiting how we have been completing wells and what we might be able to improve. These improvement areas often involve some sort of trade-off between well deliverability and well/completion costs (in terms of equipment and rig time to deploy these various alternatives). I suspect that we all have been involved with completions where these two areas are debated. In my experience, it seems that much of our discussion revolves around what the various participants “feel” is the best approach. Much of the decision eventually hinges on what we will do in the short term (deployment) rather than in the long term (deliverability). The reason is, in my opinion, that we are fairly sure about the near-term items (related to cost) but often very uncertain about the longer-term items (deliverability as a result of how well we deployed the lower completion). Why is it that many of our completion quality decisions are focused on cost and not deliverability? I can think of two primary reasons: (a) We lack the metrics to support our decisions and (b) we do not have consistent practices (e.g., laboratory and design work, deployment processes) across our wells to allow us to compare our results. I suspect that you can think of others. Both of these areas offer improvement opportunities. For those of us who have a robust set of metrics to evaluate our overall sand-control planning and deployment process, if the preparation work (e.g., core testing, compatibility testing, equipment selection) is not carried out in a consistent fashion, the variation in results as depicted in our metrics would not lead us to a specific course for improvement because the variation from well to well might simply be explained away by the differences in planning and execution. Given the preceding idea, could the development of consistent practices be a critical first step on our journey toward achieving improvements in completion quality? The list of those practices that we should carry out in a consistent manner is quite long. For sand-control applications, we could start with those activities that occur early in the design process. After reviewing the many high-quality technical papers written over the past year, I have found a few that I think offer a good place for you to start your journey toward consistency. The three summarized papers are all related to the selection of proppant and screens in your sand-control completions. I am not promoting any one of these papers over the others. However, I am suggesting that whatever your organization does in this area, your organization should do it consistently. You may find your organization’s new, preferred approach to proppant and screen selection in one or more of the presented articles. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 178966 Sand-Retention Testing: Reservoir Sand or Simulated Sand—Does It Matter? by Tracey Ballard, Weatherford, et al. SPE 179036 Sand-Screen Design and Optimization for Horizontal Wells Using Reservoir Grain-Size-Distribution Mapping by Mahdi Mahmoudi, University of Alberta, et al.

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)0.015
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.007
Scholarly communication0.0170.017
Open science0.0030.010
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0350.008

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 head0.192
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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