Synthetic core from conventional logs: A new method of interpretation for identification of key reservoir properties
Bibliographic record
Abstract
Abstract We developed a unique method to generate reservoir attributes by creating an artificial core for those wells that have no core, but that have gamma, neutron, and density logs. We examined sedimentary facies distributions, reservoir attributes, and mechanical parameters of the rock for noncored wells to increase the data density and improve the understanding of the reservoir. This method eventually helps to improve high-resolution 3D geocellular models, geomechanical models, and reservoir simulation in reservoir characterization. Artificial or synthetic cores are created using a single curve that builds facies templates using the information from the cores of nearby offset wells, which belong to the same depositional environment. The single curve, called the fine particle volume (FPV), is the average of two shale volumes calculated from the gamma-ray log and from a combination of neutron and density logs. Using facies templates, the FPV curve builds the synthetic core for geocellular modeling and reservoir simulation, and it represents the sedimentary facies distribution in the well with all the reservoir attributes obtained from laboratory data of the original core. The vertical succession of the synthetic core has the characteristics of actual sedimentary facies with reservoir attributes such as porosity, permeability, and other rock properties. The result of creating the synthetic core was validated visually and statistically with the actual cores, and each of the cored wells was considered as a noncored well. The limitation of this method is associated with the accuracy of the logging data acquisition, normalization factors, and facies template selection criteria.
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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 | 0.000 |
| 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".