Integrated Modeling for Assisted History Matching and Robust Optimisation in Mature Reservoirs
Bibliographic record
Abstract
Abstract History matching and production optimization are the important keys in reservoir modeling. Reservoir geology plays a crucial role in these complicated processes and the oil and gas operators highly demand for a fast, accurate and effective integration of geology and reservoir engineering that is still limited in the past. This paper aims to present: (1) a modeling approach that automatically integrates geological modeling software, a reservoir simulator, and history matching and optimization software in a closed-loop; (2) the advantages of an assisted history matching approach; (3) a robust optimization workflow based on multiple geological realizations. This new approach was successfully applied in a Brugge reservoir. We first present a systematical workflow of the integrated modeling that allows us to capture the crucial effects of geology. Based on this approach, multiple geological realizations are geostatiscally generated for history matching, robust optimization and uncertainty assessment. We utilize the benefits of coupling the geological modeling software, the reservoir simulator and the optimization tool together. The Designed Exploration and Controlled Evolution optimization method is used to perform the history matching. In the history matching process, the optimizer invokes geological software with new variogram parameters to calculate the reservoir properties. Finally, a robust optimization approach based on multiple geological realizations is introduced to overcome the current weakness of optimization based on single realization. The closed loop modeling approach is proven a powerful tool to improve the modeling quality and reduce the time and engineering efforts for capturing the critical effects of reservoir geology in complex sandstone reservoirs. Using this closed loop modeling, we successfully perform an assisted history matching of the secondary waterflooding process. This method effectively accounts for the uncertainty of geological characteristics in terms of facies proportion and spatial distribution. The properties of each facies were controlled by both blocked data histograms and the vertical trend. The results of history matching show that the misfit of objective functions was reduced from 16.45% to 6%. Finally, we optimize the rates of thirty injection and production wells over the life of a reservoir, with the objective to maximize the average NPV based on the geological realizations that the history misfit is less than 7% rather than using a single realization. The results show that the robust optimization significantly improve the expected NPV and reduce substantially the risk associated with geological uncertainties. This paper presents an efficient modeling and optimization approach under geological uncertainties by integrating various simulator's available in the industry. The innovative closed loop modeling workflow together with an assisted history matching and robust optimization provides a means of optimizing recovery and assessing uncertainties of both secondary and tertiary EOR processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".