Determination of stabilization time during stress-sensitivity tests
Bibliographic record
Abstract
Previous studies proved that rock properties such as permeability, porosity, and well-logging properties can be changed when effective pressure increases (Labuz and Biolzi, 2016; Xiao et al., 2016; Dou et al., 2016; Miah et al., 2020; Han et al., 2021). The damage degree of permeability during such process is usually referred to as stress sensitivity. Stabilization time can be used to quantify the delayed stress sensitivity phenomenon. It characterizes how much time is required for a given core to reach an unchanging permeability level when the confining pressure is changed from a lower pressure to a higher pressure. However, few studies explore the mechanisms behind the observation that it takes an extra-long time for a low-permeability core to reach an unchanging permeability level when the confining pressure is changed from a lower pressure to a higher pressure. Most of stress sensitivity tests are terminated before reaching the stabilization stage, which leads to an underestimation of the actual permeability damages. In this study, we make a hypothesis that the delayed stress sensitivity can be correlated with the pore-scale properties of the reservoir rocks. In this study, mercury intrusion porosimetry (MIP) tests and tri-axial stress-sensitivity tests have been conducted on twelve core samples. MIP tests are used to measure the pore size distributions and to estimate the pore structure property. Using a trial-and-error approach, we develop an empirical method to split the pores in a given core sample into large pores and small pores based on the pore size distribution charts. Here we divide the pores into large and small pores. Our study find that the delayed stress sensitivity is strongly correlated with the pore structure of core samples. Once the pores are split into large pore and small pores, we can further calculate the area ratio of the large pores to the small pores. During each tri-axial stress-sensitivity test, we monitor the variation of permeability versus time. By analyzing the permeability variation data, we can determine the stabilization time, i.e., the time required for the permeability to reach a constant value when confining pressure changes to a higher level. We also record the cumulative stabilization time as a function of the confining pressure. The experimental results indicate that a core sample with a larger area ratio of large pores to small pores has a shorter stabilization time, while a core sample with a smaller area ratio of large pores to small pores has a longer stabilization time. Based on the experimental results, we develop a novel empirical model that can be used to predict the stabilization time required during the stress-sensitivity tests of a given rock sample.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".