A Multiple-Porosity Model for Evaluation of Giant Naturally Fractured Gas Sandstone Reservoirs in Bolivia
Bibliographic record
Abstract
Abstract A multiple-porosity petrophysical model that honours heterogeneity and the complex geologic characterization of a giant gas reservoir in Bolivia has been developed. The model handles five different porosity types: macrofracture, low conductivity fracture, microfracture, shale, and dispersed and intergranular porosity. Geological evaluation indicates that these porosities have some degree of connectivity. Due to the various porosity components, the model permits a more accurate estimation of the cementation exponent (m) and provides a better understanding of the gas storage and water saturation distribution in the reservoir. The model also has the capability to account for lithological variations throughout the field. There is a wide variety of available models for estimating petrophysical properties, but none was found that could represent the available data for this field realistically. The Borai correlation, developed exclusively with data from Abu Dhabi carbonate reservoirs, has been used in the past to evaluate the Bolivian reservoir. As a consequence, we undertook the petrophysical analysis from a different perspective and developed the proposed model. Taking into account the various porosity types, lithology and structures depicted in the field geological characterization has been essential to develop the solid petrophysical model presented in this study. This in turn is an important aid for understanding reservoir behaviour. The proposed multiple-porosity model allows sound characterization of the Bolivian gas reservoir considered in this study because it is strongly linked to the field geological heterogeneity. We anticipate that given the flexibility of the model, it will be applicable for characterization of sandstone reservoirs with similar characteristics in other regions around the world.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".