Real-Time Collaboration—Efficient Problem Solving and Extending Resources
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".