Application of Machine Learning to Create a Discrete Fracture Network Model for Utah FORGE Fracture Injections
Notice bibliographique
Résumé
Abstract A method to process microseismic event locations from three injections into the Utah FORGE 16A(78)-32 geothermal well has been developed as part of the 2023 SPE Geothermal Datathon. One objective of the datathon was to develop methods using a few tunable parameters that are capable of multiple realizations of the Discrete Fracture Network (DFN). The method uses open-source software tools and comprises seven steps. The first step is to calculate the square-root of elapsed time from the first event of each stage. The next step is to use DBSCAN (Density Based Spatial Clustering of Applications with Noise) on this RootTime variable, followed by the application of DBSCAN to the spatial variables in each time slice. Each of the resulting clusters is analyzed by principal component analysis to generate fracture planes. DBSCAN leaves multiple outliers that are then harvested using two methods. Criteria are provided to fuse fractures together that are close spatially. The final step is to consider if connective fractures are required to ensure communication of the fracture network with the perforated interval. The Utah FORGE dataset comprises 2798 event locations from three injections. The analysis in time yielded 54 clusters of data, and the spatial analysis then provided 73 distinct fractures, with a residue of 25% outliers. Outliers were harvested in two steps: first, capturing outliers that were adjacent to mapped fractures, and then evaluating the remaining outliers for individual fracture planes using relaxed DBSCAN parameters. After these two steps, the outlier population was reduced to less than 4%, and the total number of mapped fractures grew to 87. It was recognized that fractures can propagate across time slices, so a fracture fusion step was conceived to combine subparallel fractures that were indistinguishable from each other based on error analysis. This was particularly necessary for Stage 3 that had mostly vertical fractures. In this step, 24 fractures were combined, resulting in a total of 63 fractures in the DFN. In the final step, it was recognized that there were no fracture intersections with the perforated interval for Stage 2, and thus an aseismic flow path was inferred. A vertical and a horizontal fracture were inserted to represent this flow. Each DBSCAN application has two input parameters, resulting in possibly many clusters and multiple outliers. The development of steps to harvest outliers and fuse adjacent fractures were conceived to utilize as much data as possible and to recognize the relative errors in event locations. With regards to the Datathon goal of achieving an automated processing sequence, the algorithm runs without manual intervention once the user has chosen four parameters for each stage: the minimum number of points in a cluster and the accepted percentage of outliers for each of the time and spatial clustering steps. The calculated dominant fracture azimuth of N-20-E compares favorably with data from the field, providing some indication of the quality of the results.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».