Reducing Simulation Time in a Huff-And-Puff Gas Injection Project in Complex Shale Reservoirs: Sequence-Based Proxy Multi-Porosity Reservoir Simulator
Bibliographic record
Abstract
Abstract The objective of this project is to explore cutting-edge sequence-based machine learning models commonly used in language processing to reproduce a multi-porosity reservoir simulator. The proposed method integrates advanced techniques to significantly reduce the numerical simulation time and improve the decision-making process for Huff and Puff (H-n-P) gas injection optimization in shale reservoirs. The proposed approach follows three crucial steps to predict an output sequence given an input sequence: 1) the simulation results should be validated against actual data, 2) train and validate a machine learning model using simulation results from either commercial or in-house numerical simulators, 3) exhaustive exploration of hyperparameter tuning and selection of machine learning techniques, such as sequence-to-sequence (Seq2Seq), Luong attention and ConvLSTM. The proxy model considers as input variables well control parameters such as injection and production periods, number of cycles and gas injection rates to estimate the proxy model results. The multi-porosity proxy reservoir simulation model is a complementary tool that integrates numerical simulation and data-driven techniques. Although tuning the model typically demands significant time, it can speed up the simulation time up to 20,000X allowing for generating hundreds or even thousands of scenarios at the expense of accepting a reduction in the accuracy of the results in a matter of minutes. One of the most notable findings is that considering a small training dataset, the proxy model can reproduce the capabilities for predicting oil production in complex low and ultra-low permeability reservoirs with significantly reduced error, relative to the multi-porosity reservoir simulator. Finally, the possibility of reproducing a considerable number of scenarios in minutes opens the door to exploring different well control configurations such as injection and production periods, number of cycles and gas injection rates. The novelty of the proxy multi-porosity reservoir simulator is to notably accelerate the numerical simulation time by using techniques capable of solving sequence learning problems in which the output is dependent on previous outputs.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".