On possible fluid detection in equivalent transversely isotropic media
Bibliographic record
Abstract
ABSTRACT We consider a transversely isotropic medium that is long‐wave equivalent to a stack of thin, parallel, isotropic layers and is obtained using the Backus average. In such media, we analyse the relations among anisotropy parameters; Thomsen parameters, ε and δ, and a new parameter ϕ. We discuss the last parameter and show its essential properties; it equals 0 in the case of isotropy of equivalent medium and/or constant Lamé coefficient λ in layers. The second property occurs to make ϕ sensitive to variations of λ in thin‐bedded sequences. According to Gassmann, in isotropic media the variation of fluid content affects only the Lamé coefficient λ, not μ; thus, the sensitivity to changes of λ is an essential property in the context of possible detection of fluids. We show algebraically and numerically that ϕ is more sensitive to these variations than ε or δ. Nevertheless, each of these parameters is dependent on the changes of μ; to understand this influence, we exhibit comprehensive tables that illustrate the behaviour of anisotropy parameters with respect to specific variations of λ and μ. The changes of μ in layers can be presented by the Thomsen parameter γ that depends on them solely. Hence, knowing the values of elasticity coefficients of equivalent transversely isotropic medium, we may compute ϕ and γ, and based on the aforementioned tables, we predict the expected variation of λ; in this way, we propose a new method of possible fluid detection. Also, we show that the prior approach of possible detection of fluids, proposed by Berryman et al., may be unreliable in specific cases. To establish our results, we use the Monte Carlo method; for the range and chosen variations of Lamé coefficients λ and μ – relevant to sandstones – we generate these coefficients in thin layers and, after the averaging process, we obtain an equivalent transversely isotropic medium. We repeat that process numerous times to get many equivalent transversely isotropic media, and – for each of them – we compute their anisotropy parameters. We illustrate ϕ, ε and δ in the form of cross‐plots that are relevant to the chosen variations of λ and μ. Additionally, we present a table with the computed ranges of anisotropy parameters that correspond to different variations of Lamé coefficients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".