Development of advanced predictive software for optimizing oil and gas well cementing
Bibliographic record
Abstract
Primary cementing is a critical process in oil and gas well construction, involving the placement of a cement sheath in the annulus between the casing and the formation to ensure well integrity. Over the past two decades, the complex fluids research group at UBC has developed advanced mathematical and computational models to simulate this process, matched with extensive laboratory scale experiments. These models account for the complex displacement of Newtonian/non-Newtonian fluids in inclined, non-concentric, narrow annuli as well as in pipes, and have employed complex rheological fluid models in 1D, 2D and 3D simulations.A current focus of the group is on translating these research-grade models into robust, user-friendly predictive software written in Python and Julia, primarily targeted at stakeholders in Western Canada, i.e. land-based wells. Some challenges faced include data transfer between components of simulation results, numerical precision, measurement system management, and versatility of well definition. Our ultimate goal is to provide Canadian energy stakeholders with a powerful tool to optimize primary cementing operations, improve wellbore integrity, and reduce environmental risks.The computational core contains the numerical models, which are packaged and embedded. The physical models handle laminar, transitional, and turbulent flow regimes, significant buoyancy, geometric and dispersion effects, giving insights into observations from field data, laboratory experiments, and high-fidelity 3D simulations. Data input from surveys, previous casings and open hole caliper readings is combined with centralization computations to give unparalleled predictions of irregular hole and annuli along the flow path.In this talk we will show the evolution of the model, highlighting the challenges of adapting the research codes into a friendly graphic user interface that can be adopted by engineers in the field.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".