Determination of Rock Compressibility in Unconsolidated Sand in Heavy and Extra-Heavy Oil Fields in Mexico
Bibliographic record
Abstract
Abstract Unconsolidated sands in heavy and extra-heavy oil fields in Mexico have significant potential that has not been fully evaluated yet. Thus, this paper examines petrophysics and geomechanical aspects with a view to estimating rock compressibility. This is important since determining this parameter from cores has proved to be difficult many times as the samples tend to collapse easily during laboratory experiments. The proposed method uses an empirical correlation for estimating Biot coefficient (Li et al., 2020) and more established geomechanical equations written in such a way as to allow the estimation of several types of compressibilities including: bulk compressibility, uniaxial bulk compressibility, pore compressibility, uniaxial pore compressibility, and pore compressibility under hydrostatic load. The data are loaded on a Pickett plot (1966, 1973) to demonstrate the value of pattern recognition. There are several intermediate results from calculations leading to the compressibilities mentioned above. These include process speed (ratio of permeability and porosity), pore throat aperture in microns at 35 percent cumulative pore volume (rp35), water saturation (Sw), mercury-air capillary pressure (pc), pore throat apertures (rp) at different water saturations, Biot coefficient (α), Poisson's ratio (PR), shear modulus (G), Young's modulus (YM), and fluid compressibility (cf). An important observation is that although use of the equations presented in the paper are straight forward and lead to quick calculation of all parameters mentioned above, it is likely that calculations from well logs without using pattern recognition may lead to uncertain results. The novelty of the paper is developing a methodology for calculating diverse types of rock compressibilities in unconsolidated sandstone reservoirs. Application of the methodology can lead to improved calculated recovery factors of unconsolidated sandstone reservoirs in heavy and extra-heavy oil fields in Mexico by at least 10%.
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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 | 0.001 |
| 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".