Vaca Muerta: Integrated Characterization of Natural Fractures and Oil Wettability Using Cores, Micro-Resistivity Images and Outcrops for Optimizing Landing Zones of Horizontal Wells
Bibliographic record
Abstract
Abstract Understanding natural fracture width distribution, and pore sizes relation to thermal maturity, permeability and wettability is important in assessing shale reservoir quality and determining the productive landing zones for horizontal wells. Natural fractures and pore systems in Vaca Muerta are complex with significant lateral and vertical variations. This study provides an integrated characterization using cores, micro-resistivity images and outcrops that reveal the vertical variability of natural fractures and oil-wet characteristics of Vaca Muerta shale. The proposed method describes first the distribution of fracture widths from cores, micro-resistivity images and outcrops using a Variable Shape Distribution (VSD) model. The VSD provides a good fit of the data, which improves fracture width and intensity prediction. Subsequently, porosity, Total Organic Carbon (TOC) and water saturation (Sw) are modeled and calibrated with core data. Values of the porosity exponent m and the water saturation exponent n reflect the complexity of the pore system and wettability characteristics of Vaca Muerta. The method also incorporates for the first time, thin bed heterogeneity that comprises calcite beef, ash beds and nodules. Results indicate that fracture widths at Vaca Muerta range between 0.0003 and 2 mm for cores, 0.01 and 2 mm for micro-resistivity images, and 0.0003 and 7 mm for outcrops. The VSD captures the entire distribution of cores, images and outcrops, which allow pragmatic fracture width extrapolation. The physical widths can also be used to generate synthetic production logs (PLT) that indicate relative productivity from fractured intervals. The study reveals that better reservoir quality lies in the deeper organic-rich units of the Lower Vaca Muerta (LVM) shale. The LVM has lower Sw, larger pores, higher TOC, and greater natural fracture intensity. Pickett plots indicate decreasing m and increasing n values with depth. This suggests increasing natural fractures intensity and oil wettability towards the LVM, which is corroborated by cuttings descriptions, micro-resistivity images and a published Scanning Electron Microscopy (SEM) study. All these findings support the relation between pore sizes and thermal maturity, permeability and wettability. Finally, the study highlights the importance of incorporating thin bed heterogeneity in the analysis, due to its high occurrence in the organic-rich unit. The integrated analysis using cores, micro-resistivity images and outcrops reveals the variability of natural fracture intensity and oil-wet characteristics in each stratigraphic unit of the Vaca Muerta shale. The analysis considers, for the first time, the internal anatomy of thin bed heterogeneity. This methodology proves powerful for understanding the complex Vaca Muerta shale and for optimizing the landing zones of horizontal wells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".