Standardization Of Procurement Equipment Specifications: Establishing A Strong Foundation For Oil & Gas Capital Project Development And Delivery
Bibliographic record
Abstract
Abstract Industry-wide standardization is needed for operators to manage the $3 trillion in CAPEX expenditure forecast for 2018-2025 (Global Data, 2018), while maintaining competitiveness and mitigating risks. With the backing of the World Economic Forum, Joint Industry Program 33 (JIP 33) was initiated to drive industry-level standardization for the procurement of equipment items, moving the industry structurally – across the value chain - towards common engineering designs and solutions established by means of cross-company collaboration. Fourteen (14) standard specifications have been delivered to date. Their adoption by 12 major operators is being measured and is progressing well. This enables the program to capture and evaluate successes, challenges, key learnings and feedback. The program is also establishing a process for future maintenance of JIP33 specifications in order to achieve sustained benefits. A further 35-40 supplementary specifications are being developed, based on recognized industry or international standards by the end of 2020. This paper will present the status of the JIP33 program and showcase some of its early successes, challenges and learnings. Experience with digital requirement developments will be shared, as well as learnings from the adoption and use of the Phase 1-2 specifications by JIP33 members.
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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 | 0.119 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".