A Practical Approach to History-matching Large, Multi-well SAGD Simulation Models: A MacKay River Case Study
Bibliographic record
Abstract
Abstract History-matching is commonly used to calibrate numerical simulation models to validate geological and reservoir engineering inputs and ensure the reliability of model predictions. While history-matching can yield a powerful forecasting tool, thermal SAGD history-matching presents a host of challenges, particularly in terms of computational time and numerical tuning difficulties associated with large thermal simulation models. Typically, single well or small multi-well models are developed and calibrated to generate meaningful production forecasts. However, these predictions are only valid prior to steam chamber coalescence and the models are insufficient for life-of-well forecasting. Hence, it is necessary to build and calibrate large multi-well simulation models within a reasonable timeframe. The oldest SAGD wells at Suncor's Mackay River (MR) property are part of the initial Phase 1 development. This phase consists of 25 well pairs that commenced steam injection in September 2002. This paper presents a calibration process for Phase 1 simulation model, generated through Suncor's geostatistical modeling process, incorporates more than 9 years of production history and contains approximately 1.4 million active cells. The process described has several components: 1) rigorous data quality control, 2) establishing appropriate boundary conditions, 3) numerical tuning, and 4) a focus on global rather than detailed local changes. Using the process described herein, a Phase 1 history match was achieved which honours actual field rate and pressure history. The history match results are presented on well/pattern/phase basis. This process has subsequently been applied to MR Phase 2 and 3 with significant efficiency improvements. A process is presented that addresses many of the issues that arise in validating and history-matching large, complex simulation models. It is shown that history-matching multi-wells SAGD performance with a long operation history is feasible, practical, and useful. The calibrated modeling processes gives confidence in understanding the reservoir and reduces the uncertainty of future forecasting results.
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 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 | 0.000 |
| 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".