Creating Open Source Models, Test Cases, and Data for Oilfield Drilling Challenges
Bibliographic record
Abstract
Abstract The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish their results through peer-reviewed literature. Several online meetings are planned to create this coalition, establish a charter, and layout the guiding principles. Multiple support avenues are proposed to sustain the effort such as: annual user group meetings, create a SPE Technical Section, and initiating a Joint Industry Program (JIP). The Open Porous Media Initiative is just one example of how this could be organized and maintained. As a starting point, this paper reviews existing published drilling models and highlights the similarities and differences for commonly used drillstring hydraulics, dynamics, directional, and bit-rock interaction models. The key requirements for re-usability of the models and code are: 1) The model itself must be available as open source, well documented with the objective and expected outcomes, include commented code, and shared in a publicly available repository which can be updated, 2) A user's guide must include how to run the core software, how to extend software capabilities, i.e., plug in new features or elements, 3) Include a "theory" manual to explain the fundamental principles, the base equations, any assumptions, and the known limitations, 4) Data examples and formatting requirements to cover a diversity of drilling operations, and 5) Test cases to benchmark the performance and output of different proposed models. In May 2018 at "The 4th International Colloquium on Non-linear dynamics and control of deep drilling systems," the keynote question was, "Is it time to start using open source models?" The answer is "yes". Modeling the drilling process is done to help drill a round, ledge free hole, without patterns, with minimum vibration, minimum unplanned dog legs, that reaches all geological targets, in one run per section, and in the least time possible. An open source repository for drilling will speed up the rate of learning and automation efforts to achieve this goal throughout the entire well execution workflow, including planning, BHA design, real-time operations, and post well analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".