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Record W4392187460 · doi:10.2523/iptc-23195-ms

Risk Reduction and Continuous Improvement in the Deployment of Motorized Intelligent RSS in Unconventional Plays

2024· article· en· W4392187460 on OpenAlexaboutno aff
I. Garda

Bibliographic record

VenueInternational Petroleum Technology Conference · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsRSSSoftware deploymentReduction (mathematics)Computer scienceEmbedded systemComputer securityOperating system

Abstract

fetched live from OpenAlex

Abstract Introduction of new technology to drill faster, high-quality wellbores is always at demand for service companies. Many vendors fail to deliver consistent results by applying a fit-for-all strategy to every unconventional project with distinct challenges. This paper describes the successful introduction of a motorized rotary steerable system in the Montney shale play in Canada, after its successful introduction in the Vaca Muerta shale play in Argentina, under a completely different set of drilling challenges. A directional drilling vendor has developed a robust and intelligent RSS that can address modern unconventional drilling requirements. By utilizing state-of-the-art sensor capabilities, a bidirectional communication system that can receive and confirm commands near instantaneously, and a flexible aggressiveness controller, this system is able to immediately correct trajectory without the need to wait for tool response, and to sustain steering capability even in presence of severe vibration. Subsequently, with the application of a management system and global support, which includes drilling advisors, materials, control systems and sensor physics experts, an analytical approach is applied to every different project. With this enhanced engineering support, and customer collaboration to address new challenges, the directional drilling service company can improve SQ metrics for RSS services, while evaluating the effect of increased stress cycles from MARSS (Motor Assisted RSS) applications in actuation systems and modify the design accordingly in less than two months. The Vaca Muerta shale play in Argentina and the Montney shale play in Canada are compared. The former is developed with lateral extensions between 1.5 to 2 miles where mud weights of up to 15.5 ppg are usually prone to generate erosion in downhole tools. This factor, combined with the requirement for Managed Pressure Operations and extensive wellbore conditioning before trips, can lead up to 48 hours of downtime after a downhole tool failure. The Montney, on the other hand, is currently extending the drilling envelope to over 3-mile laterals in shale and siltstone formations. It is prone to generate damage due the presence of high axial and torsional vibrations in a very high ROP environment. This intelligent rotary steerable system assisted with mud motor was successfully introduced in these completely different unconventional basins with very distinct drilling challenges, delivering the first 5 wells of each project to total depth with zero trips for failures. Furthermore, it allowed a major North American operator to set a length and ROP record in the Canadian Montney Basin. Additionally, the management system and global support interaction allowed for a drastic reduction in tool wear from first to last run, maximizing asset value and tool turnaround. The combination of new drilling technology and the described methodology for its introduction into challenging unconventional basins is proven to reduce the risk of failure, increasing SQ metrics and on-bottom time while achieving new performance benchmarks, challenging the status-quo of fit-for-all solutions in RSS market.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.340
Teacher spread0.294 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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