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Record W1983895639 · doi:10.2118/112141-ms

Real-Time Collaboration—Efficient Problem Solving and Extending Resources

2008· article· en· W1983895639 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIntelligent Energy Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceAsset (computer security)Context (archaeology)Key (lock)Component (thermodynamics)Service (business)Knowledge managementBusinessComputer securityMarketing

Abstract

fetched live from OpenAlex

Abstract To meet the challenges to the industry of increasing hydrocarbon demand, increasing well complexity, reduced employee experience levels and the large physical distances between operational centers, advances in digital technologies are being increasingly leveraged by both operator initiatives and service company initiatives such as Halliburton's Digital Asset. Terms such as "smart wells" and "real time" have become more commonplace. Data is being generated faster than ever. The ability to interpret this data, model the data and implement optimized solutions in real time is critical to operational success. The demands placed on operating in a cost efficient manner, with greater returns on investment are ever present. The use of a Knowledge Management collaboration tool, a key component of the Digital Asset, helps to meet these challenges by providing a real time collaborative environment which spans global operations, supports and develops synergies between multiple disciplines and transcends geographical and language barriers. Through its use an intentional shift in focus has taken place from centrally located sources of expertise to virtual ones. Virtual centers of collaboration empower users to collaborate, problem solve and share knowledge on demand. Any user, i.e. employee, can rapidly access the global expertise needed to put well challenges, potential solutions and increasing volumes of data and information in appropriate context. Through access to these extended resources employees can solve problems more efficiently and offer better solutions. Technical experts can cover more ground. Collaboration is facilitated by dedicated personnel who maintain a vital link with local, regional, and global technology leaders. Examples from Canada, where the use of this approach contributed to an HTHP well being saved, along with an estimated cost of $15 million, from China where urgent advice was delivered to a rig experiencing an underground blowout and from Brazil where global experts collaboratively contributed to solving a wellbore stability problem will demonstrate how real time collaborative solutions are developed and moved from the virtual to real world environment to improve operational service delivery to external clients in the global market place. Lessons learned, best practices and strategies employed to engage users in the use of this collaborative environment are outlined.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.575

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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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