Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs
Bibliographic record
Abstract
Conventional simulation of fractured carbonate reservoirs is computationally expensive because of the multiscale heterogeneities and fracture–matrix transfer mechanisms that must be taken into account using numerical transfer functions and/or detailed models with a large number of simulation grid cells. The computational requirement increases significantly when multiple simulation runs are required for sensitivity analysis, uncertainty quantification and optimization. This can be prohibitive, especially for giant carbonate reservoirs. Yet, sensitivity analysis, uncertainty quantification and optimization are particularly important to analyse, determine and rank the impact of geological and engineering parameters on the economics and sustainability of different Enhanced Oil Recovery (EOR) techniques. We use experimental design to set up multiple simulations of a high-resolution model of a Jurassic carbonate ramp, which is an analogue for the highly prolific reservoirs of the Arab D Formation in Qatar. We consider CO 2 water-alternating-gas (WAG) injection, which is a successful EOR method for carbonate reservoirs. The simulations are employed as a basis for generating data-driven surrogate models using polynomial regression and polynomial chaos expansion. Furthermore, the surrogates are validated by comparing surrogate predictions with results from numerical simulation and estimating goodness-of-fit measures. In the current work, parameter uncertainties affecting WAG modelling in fractured carbonates are evaluated, including fracture network properties, wettability and fault transmissibility. The results enable us to adequately explore the parameter space, and to quantify and rank the interrelated effect of uncertain model parameters on CO 2 WAG efficiency. The results highlight the first-order impact of the fracture network properties and wettability on hydrocarbon recovery and CO 2 utilization during WAG injection. In addition, the surrogate models enable us to calculate quick estimates of probabilistic uncertainty and to rapidly optimize WAG injection, while achieving significant computational speed-up compared with the conventional simulation framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".