Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5
Bibliographic record
Abstract
Abstract Fractures in reservoirs have a profound impact on hydrocarbon production operations. The more accurately fractures can be detected, the better the exploration and production processes can be optimized. Therefore, fracture detection is an essential step in understanding the reservoir’s behavior and the stability of the wellbore. The conventional method for detecting fractures is image logging, which captures images of the borehole and fractures. However, the interpretation of these images is a laborious and subjective process that can lead to errors, inaccuracies, and inconsistencies, even when aided by software. Automating this process is essential for expediting operations, minimizing errors, and increasing efficiency. Although there have been some attempts to automate fracture detection, this paper takes a novel approach by proposing the use of YOLOv5 as a deep-learning (DL) tool to detect fractures automatically. YOLOv5 is unique in that it excels at speed, training, and detection while maintaining high accuracy in fracture detection. We observe that YOLOv5 can detect fractures in near real time with a high mean average precision of 98.2, requiring significantly less training than other DL algorithms. Furthermore, our approach overcomes the shortcomings of other fracture detection methods. Our method has many potential benefits, including reducing manual interpretation errors, decreasing the time required for fracture detection, and improving fracture detection accuracy. Our approach can be used in various reservoir engineering applications, such as hydraulic fracturing design, wellbore stability analysis, and reservoir simulation. By using this technique, the efficiency and accuracy of hydrocarbon exploration and production can be significantly improved.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".