Three-Dimensional Geological Engineering Double Desert Evaluation of Low-Permeability Sandstone Reservoirs
Bibliographic record
Abstract
Offshore low-permeability sandstones with poor physical properties and high nonhomogeneity are effective modification measures using hydraulic fracturing, but they are costly and risky, and conducting fracability evaluations is a necessary measure to ensure the successful implementation of low-permeability sandstones. Therefore, it is necessary to carry out geological and engineering sweet spot evaluations. The engineering sweet spot is mainly aimed at forming a complex subnetwork, considering factors such as energy brittleness, fracture mechanics, quantification of natural fractures, etc., along with adopting the hierarchical analysis method and entropy weighting method to establish a model for evaluating the fracability of the fracture network. The geological sweet spot is based on logging interpretation data, considering mud, pore space, and gas-bearing factors, and the factor analysis method is used to establish a single-well geological sweet spot evaluation model. Based on the previous geological and engineering sweet spot evaluation model and combined with seismic wave inversion, a three-dimensional (3D) geological and engineering double sweet spot evaluation model was established, and the reservoir types were subdivided into Class I, Class II, and Class III. In addition, the capacity prediction under different reservoir types was carried out, and the sandstone reservoir double-sweet spot evaluation template was established by combining the geological and engineering sweet spot evaluation results corresponding to different reservoir types. Taking the DF13-1-1 well as an example, the geoengineering fracability index is calculated, and the production is predicted and compared with the actual production to verify that the model is more reliable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".