Vaca Muerta: Improved Fracture Width Distribution and Classification of Natural Fracture Widths Based on Outcrops, Cores, and Microresistivity Images Data
Bibliographic record
Abstract
Summary Natural fractures in Vaca Muerta are very complex, such that their fracture width distributions cannot be analyzed simply by considering normal, log-normal, or log-log distributions. Natural fractures are commonly classified as macrofractures or microfractures; however, no consistent fracture width is attached to those fractures. In this study, two new approaches are proposed; an improved fracture width distribution and a classification for natural fractures that encompasses all physical widths found in petroleum reservoirs. The method developed in this study first evaluates the distribution of natural fracture widths from outcrops, cores, and microresistivity images of Vaca Muerta shale. An improved fracture width distribution is established through a variable shape distribution (VSD). The model provides a good fit, even if the shape of the distribution deviates from generally accepted distributions. This improves the accuracy of fracture width and intensity prediction, which is useful in generating synthetic production logging tools (PLTs) to estimate productivity from fractured intervals. Subsequently, a consistent classification for natural fractures is introduced to cover all fracture widths found in petroleum reservoirs. Results indicate that fracture widths in Vaca Muerta shale range between 0.0003 mm and 7 mm for outcrops, 0.0003 mm and 2 mm for cores, and 0.01 mm and 2 mm for microresistivity images. The VSD model provides a good fit of fracture widths from the three sources, without truncating any of the data. Truncation of data is usually required when using generally accepted distributions. With this improved distribution, size pattern extrapolation can be performed with greater accuracy. The physical widths can also be translated into hydraulic apertures to generate theoretical PLT. This is useful for estimating relative petroleum production potential from each fractured interval and for identifying future refracturing zones. Additionally, the study gives origin to a consistent classification of fracture widths that has application in Vaca Muerta and other oil and gas reservoirs. Five subclasses are introduced, which are megafractures (> 10 mm), macrofractures (1–10 mm), mesofractures (0.1–1 mm), microfractures (0.01–0.1 mm), and nanofractures (<0.01 mm). A careful review of the literature indicates that there is ambivalence as it is hard to find a clear and precise terminology that encompasses the entire range of fracture widths. The proposed classification eliminates that difficulty. In this paper, for the first time, a consistent fracture width classification is developed that encompasses the whole spectrum of widths found in petroleum reservoirs. It has wide application in Vaca Muerta, where widths, derived from outcrops, cores, and microresistivity image data are matched with a VSD model. Furthermore, the proposed classification can be used in other oil and gas reservoirs, thus eliminating the fracture width ambivalence found many times in the geoscience and petroleum engineering literature.
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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 | 0.000 | 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 | 0.001 |
| 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".