Strategic Digitalization in Oil and Gas: A Case Study on Mixed Reality and Digital Twins
Bibliographic record
Abstract
Many organizations are developing connected worker and digital twin solutions in mixed reality as a means to quickly train new hires while simultaneously developing assistive deep learning models for quality-control mechanisms through internal documentation, diagrams, and 3D models. However, the transformation of real-life assets and processes into digital twin counterparts is a multi-step, intensive undertaking, requiring significant domain expertise and technical know-how. Therefore, we explore the architectural and technical components required to transform the most critical assets into a digital twin that yields immediate business value. Specifically, we explore the creation of digitalization architectures with re-usable components for training not only new hires but also deep neural network-based computer vision models. In this work, we present an action research case study guided by the Project Management Body of Knowledge framework. This case study was conducted in coordination with TechnipFMC, a global leader oil and gas company, on the digitalization efforts to transition into their industrial metaverse. We developed multiple architectures to bring assembly practices into a mixed reality training solution usable by trainees and editable by domain experts in real time. Further, we generate synthetic data from the same mixed reality training environments to train object detection models on industrial components and find that the photorealistic 3D models can improve mean average precision on the real-world task by +2.5 mAP.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".