OpenLab: Design and Applications of a Modern Drilling Digitalization Infrastructure
Bibliographic record
Abstract
Abstract The transition towards drilling automation in the oil and gas industry has increased the need for digital infrastructures for development and testing of new technology. This includes infrastructures to facilitate changes in work processes and technical competences. This paper describes the design and use of OpenLab Drilling, a digital infrastructure with applications in education, technology development and testing. OpenLab Drilling offers access to a high fidelity drilling process simulator capable of simulating transient hydraulics, temperature, torque and drag, and cuttings transport. Since 2018, the infrastructure has been publicly available for students, researchers and engineers who need realistic drilling data for technology development, demonstration and education. The simulated drilling data can be accessed by several means. First, through a user-friendly web application used as a tool for teaching the physics involved in drilling operations. Secondly, drilling data can be accessed programmatically through a web API or via programming language APIs written in MATLAB, Python and .NET. Thirdly, OpenLab offers a fast communication interface that can be used for applications that are closer to hardware, and which require a realistic Hardware in The Loop (HIL) infrastructure. This paper describes the objectives of OpenLab as a project, its system architecture, its simulation capabilities, the design of its web application, and its various communication interfaces. The paper also presents projects that uses OpenLab in education, research on machine learning, semantical representation of drilling data, and other industrial relevant activities. The paper is naturally divided in two parts: The design of the infrastructure, and its applications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".