Fracture Identification and Comprehensive Evaluation of the Parameters by Dual Laterolog Data
Bibliographic record
Abstract
Abstract Quality and productivity of tight formations are heavily dependent on the degree of fracture development. In fact, hard and dense carbonate formations may not be considered as net pay without presence of fractures which conduct fluids towards wellbore. The evaluation of fractures is a key to reservoir effectiveness characterization for well drilling, completion, development and simulation of fractured reservoirs. Although new imaging technologies such as Formation Micro-Scanners and Imagers (FMS and FMI) provide the information of fracture properties (dip angle, porosity, aperture and permeability), image logs are very expensive and cannot be available in all wells. In this paper, fracture parameters are estimated using conventional Dual Laterolog (DLL) resistivity, which includes shallow (LLS) and deep (LLD) responses. This technique is based on electrical resistivity anomalies because of separation of shallow and deep laterolog curves. Fracture parameters that can be calculated by DLL include dip angle, aperture, porosity, permeability and cementation factor. The accuracy of the calculated parameters using DLL is validated by the results of FMI in one well in one of Iranian fractured reservoirs. Despite the image logs, the conventional DLL is routinely run in all of the drilled wells. This makes the constructed fracture model on the basis of DLLs more reliable than the corresponding model founded on limited and insufficient image logs. Furthermore, DLL has an advantage of much deeper evaluation of fractures in comparison with the immediate borehole investigation of image logs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".