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Record W4323655163 · doi:10.2118/212569-ms

Field Testing of an Automated 3D Cuttings and Cavings Measurement Sensor

2023· article· en· W4323655163 on OpenAlexaff
Sebastian Prez, Santiago Callerio, Abraham C. Montes, Çinar Turhan, Pradeepkumar Ashok, Eric van Oort, R. A. Pruitt, Taylor Thetford, Trey Peroyea, Michael Behounek

Bibliographic record

VenueSPE/IADC International Drilling Conference and Exhibition · 2023
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsAutomationField (mathematics)Field trialEngineeringDrillingSimulationComputer scienceMarine engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Observation of cuttings and cavings serves as an important early indicator for hole cleaning and wellbore instability problems. Automation of this critical monitoring process is, however, still in its infancy. This paper highlights the development and initial field testing of a new automated cuttings and cavings monitoring sensor system. Initial challenges identified in an earlier field test, and their solutions that have been incorporated into this new working field prototype, are presented. The imaging sensors selected for the prototype, including a 3D laser profile scanner and a machine vision camera, were used in an initial field trial to assess the feasibility of building a prototype that can operate in a harsh field environment. Based on that initial field trial, the prototype was built, and preliminary outdoor tests were conducted offsite to validate its performance. Following that, a second field trial at an active drilling site in West Texas was conducted to assess the implementation and performance of the prototype in the field. Practical solutions to key challenges identified in the first field trial were successfully implemented in the new prototype, and its field performance was validated in the second field trial where the prototype was integrated into the solids control system of an active drilling rig, successfully collecting data for during drilling operations. The system accurately measured the volumetric return of cuttings, and the tests demonstrate the ability to determine the cuttings size distribution and detect anomalous-sized cavings. The results can be used directly for improved hole cleaning management and stuck pipe avoidance in field operations. This paper introduces the first working prototype of a 3D-imaging cuttings monitoring system that could be taken into production for quantifying the volumetric return of cuttings on surface and providing information about the size and shape of cuttings and cavings. The development of this cuttings sensor is a major milestone in the field of drilling automation, bringing the industry closer to achieving a fully automated hole cleaning and stuck pipe prevention system.

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 score0.612
Threshold uncertainty score0.583

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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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