Calibration of Well Logs With Mini-Frac Data for Estimating the Minimum Horizontal Stress in the Tight-Gas Monteith Formation of the Western Canada Sedimentary Basin: A Case Study
Bibliographic record
Abstract
Summary The determination of the minimum horizontal (in-situ) stress (MHS) is one of the most-important aspects in the characterization of geomechanical behavior of petroleum reservoirs because a good knowledge of such stress is critical in many activities of practical importance, including design of hydraulic-fracturing treatments and estimation of the distribution of MHS for reservoir-simulation purposes. The main objective of the study is to calibrate well logs with available mini-frac data for estimating the MHS in the tight-gas Monteith formation, Nikanassin Group of the Western Canada Sedimentary Basin. For deep formations such as Monteith, the minimum stress is generally horizontal. Thus, the focus of this research is the MHS of the Monteith formation. First, actual values of the MHS at different well locations are acquired from the analysis of surface-pressure data during mini-frac treatments. Next, the estimates calculated from an existing correlation and from actual mini-frac data are matched for calibration purposes. Finally, vertical and horizontal Biot's constant values are determined to generate a correlation applicable to the Monteith formation. Vertical Biot's constant ranging between 0.1 and 0.45 is obtained in this study, whereas horizontal Biot's constant is found to vary from 0.94 to 1.0. Three vertical wells located in the same township with available compressional and shear sonic logs and mini-frac data are selected for this work. The Monteith was hydraulically fractured in these wells in isolation. This aspect is important because there are many commingled completions in the area from which selective Monteith data are not available. Five additional wells in the same general area are selected to assist in the analysis. It is concluded that assuming vertical and horizontal Biot's constants equal to 1.0 is not appropriate for the Monteith formation in the study area. The procedure presented in this paper, which uses real data, is robust and has the potential to help in obtaining more-reliable geomechanical values in other tight formations around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".