A Correlation for Estimating Biot Coefficient
Bibliographic record
Abstract
Abstract The objective of this paper is to develop an easy to use correlation for estimating Biot coefficient. This is important as Biot coefficient plays an important role in solving many practical petroleum engineering problems, including for example, design of hydraulic fracturing jobs and estimation of in-situ closure stress on proppant. The procedure for developing the proposed empirical correlation uses data from various lithologies including limestone, sandstone, shale, marble and granite. Thus, the correlation has application in conventional and unconventional petroleum reservoirs. Use of the correlation requires knowledge of permeability and porosity, data commonly available in petroleum engineering (on the other hand Biot coefficient data is almost never available). The ratio of permeability and porosity, commonly known as process or delivery speed and pore throat aperture (rp35), are input for estimating Biot coefficient from the correlation proposed in this paper. The correlation is useful in those cases where sophisticated experimental work needed for estimating Biot poroelastic coefficient is not available. Testing against various data sets indicates that the proposed correlation provides reasonable results. In the past, methods with different complexity levels have been used for estimating Biot coefficient. These have included, for example, (1) a method that requires knowledge of bulk modulus of the rock mineral and bulk modulus of the skeleton with no fluids in it, parameters that are not usually available for petroleum reservoirs, (2) a method that is based on knowledge of only porosity, (3) a method that is based on knowledge of only permeability, and (4) an approach that simply assumes that Biot coefficient is equal to 1.0 or some other number. The proposed correlation falls somewhere in the middle. It is not as simple as saying that Biot coefficient is equal to 1, or saying that it depends on only porosity, or only permeability. On the other hand, it is not as complex as requiring sophisticated laboratory work of the type mentioned in item (1) above. The novelty of this work is the development of an original easy to use correlation for estimating Biot coefficient in conventional and unconventional (tight and shale) reservoirs based on knowledge of k/ϕ and rp35. The correlation is developed in such a way that it has also application for estimating Biot coefficient in the case of unconsolidated petroleum reservoirs and oil sands. The overall approach allows integration of geomechanics with flow units, geology, petrophysics, and reservoir engineering.
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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".