Incorporating Biot Poroelastic Coefficient on Pickett Plots
Bibliographic record
Abstract
Abstract A method is presented for incorporating Biot poroelastic coefficient on Pickett plots. The method allows integration of petrophysical parameters such as water saturation, porosity and permeability with geomechanics through Biot coefficient. Pattern recognition in Pickett plots have been used historically for quick petrophysical evaluation, particularly for determination of water saturation. The procedure involves a crossplot of porosity vs. true resistivity on log-log coordinates. The method presented in this paper allows determination of Biot coefficient from the Pickett plot in addition to determination of standard petrophysical parameters. The method uses a correlation developed for estimating Biot poroelastic coefficient as a function of process speed (the ratio of permeability and porosity) and pore throat aperture (rp35). Results indicate that the proposed Pickett plots permits quick simultaneous estimation of different parameters of interest for a given interval including water saturation, porosity, permeability, pore throat aperture and Biot coefficient. The Biot coefficient correlation works for various lithologies including limestone, sandstone, shales, source rock, marble, granite, unconsolidated and oil sand reservoirs. Thus, the method has application in the case of both conventional and unconventional reservoirs. Key observations based on the proposed Pickett plot include: (1) there is a general tendency for Biot coefficient to decrease as water saturation increases, (2) there is a general tendency for Biot coefficient to increase as porosity, permeability, process speed and pore throat aperture (rp35) increase. It is concluded that the integration of petrophysical parameters and Biot coefficient provides a new valuable tool to assist in the solution of petroleum engineering problems such as hydraulic fracturing and estimation of in-situ closure stress on proppant. The novelty of this work is the development for the first time of an integrated Pickett plot that incorporates petrophysical analysis and Biot poroelastic coefficient.
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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".