An Integrated Approach for History Matching of Complex Fracture Distributions for Shale Oil Reservoirs Based on Improved Adaptive Particle Filter
Bibliographic record
Abstract
Summary The application of horizontal well drilling technology and volume fracturing technique makes the economic development of shale oil reservoirs feasible. The unknown fracture networks lead to severe nonlinearity and high uncertainty during fracture characterization. Moreover, the reservoir parameters usually exhibit a highly non-Gaussianity. Therefore, the key challenges for history matching in fractured shale oil reservoirs are effectively representing the fracture network and coping with the non-Gaussian distribution of reservoir-model parameters. In this work, a new characterization method for complex fracture networks is established, in which the distribution of connected fractures of the reservoir domain is represented by some statistical parameters such as fracture dip angle, fracture azimuth, and fracture half-length and some deterministic parameters such as the coordinates of fracture center points. In the uncertainty quantification and history-matching process, an integrated approach that combines the particle filter and an improved kernel density estimation (KDE) based on its Shannon entropy (SE) for estimating fracture distributions and physical parameters is presented. An adaptive mechanism based on Kullback-Leibler divergence (KLD) is introduced in the proposed history matching workflow, which automatically adjusts the number of particles to reduce the computational burden. Two examples of 3D shale oil production were constructed to validate the efficiency and accuracy of the proposed method. Results showed that the method was capable of capturing the main features of the fracture distributions in the reference cases. The proposed method has the potential to be applied in more complex cases such as multiple wells and multiphase flow.
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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 | 0.001 | 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".