Porosity and Density Estimation from Seismic Inversion in Tight Gas Sandstones
Bibliographic record
Abstract
Abstract A post stack seismic inversion has been applied to 2D and 3D seismic data in order to obtain a quantitative assessment of rock properties including density, compressional velocities and porosity in a tight gas sandstone. To achieve this objective, the study develops a new relationship between Acoustic Impedance (AI) and porosity. This study focuses on a post stack seismic inversion through a band limit technique that involves three major steps: 1) Derive a low frequency velocity model using sonic logs, 2) Invert the seismic traces using a recursive inversion procedure giving as a result the middle frequency model band of the AI, and 3) Combine the previous models in order to obtain the full band limit inversion product. The methodology is demonstrated using data from a tight gas reservoir formation chracterized with low porosity and permeability located at approximately 2, 000 meters (TVDSS) in the study area. From the model inversion, the acoustic impedance (AI) is compared with variations in porosity resulting in a reasonable correlation for the stratigraphic interval studied. The methodology can probably be extended to other regions around the world, which possess tight gas formations with similar characteristics to the ones described in this work.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".