Establishing a Digital Oil Field Data Architecture Suitable for Current and Foreseeable Business Requirements
Bibliographic record
Abstract
Abstract The Digital Oil Field (DOF) real time data structure as applied to drilling, reservoirs, wells surface production facilities, pipelines and downstream systems has evolved as bit of a muddle with little overall design and structure and little thought given to the underlying data foundational requirements. This has lead to disintegrated systems and inefficiency in attempts to integrate the multi-various systems and components. Current real time data standards are based on a combination of downstream and upstream proprietary vendor standards that are growing more and more higgledy-piggeldy as more systems are deployed. Aggravating the problem is the ever growing volumes of data which needs to be transformed into useful information to facilitate better and more timely decision-making. Hence the purpose of this paper is fourfold to: – Define the problem in terms of the current over-abundance of data systems and standards; – Document current and foreseeable data business requirements; – Define the required integrated data foundation capable of handling the ever growing data volumes and providing appropriate, timely and accurate information to those that need to know; – Identify the business value that can be attained with this more structured and standardized approach. The ultimate aim is to provide a solid foundation upon which the Digital Oil/Gas field can grow and flourish and a corresponding business justification.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".