Utility of instrumented indentation for the optimization of horizontal wellbore completions with examples from the Montney formation of Alberta and British Columbia
Bibliographic record
Abstract
Unconventional shale reservoirs are commonly exploited by drilling horizontal wellbores up to several kilometers in length. Hydraulic fracture completions of a wellbore are designed, in part, based on the geomechanical properties of the reservoir. The completion program is executed in a series of stages, typically spaced at regular intervals along the length of the lateral, without consideration of variable lithology and geomechanical properties that may exist along the length of the borehole. Assessing the geomechanical properties and stress conditions along the lateral has proven difficult due to the cost and challenge of obtaining core samples for analyses. Drill cuttings derived during drilling provide an opportunity to characterize the reservoir geomechanical properties, including the elastic moduli. If such small samples can be tested reliably and be shown to be scalable to the reservoir, the geomechanical variability along a wellbore can be measured and exploited with planned hydraulic fracture completions at geomechanical sweet spots. In this thesis, the utility of instrumented indentation for characterizing geomechanical variation in shale core chips and drill cuttings is evaluated. Indentation testing on mineral and shale samples assesses the effects of indentation and sample parameters on indentation results, suggesting that samples of size 841 µm, tested at 50 – 200 mN load, with at least 83 standard indentations are likely to provide repeatable and representative mean indentation results. Indentation can characterize geomechanical variability between shale core chips and drill cuttings samples with centimeter differences in sample depth. Repeatable indentation results are compared for indentation tests, as well as unoriented and oriented subsamples; oriented subsamples showed 2 – 18 % greater mean indentation modulus in samples with bedding oriented parallel to the direction of indentation, compared to bedding perpendicular, indicating the effects of mechanical anisotropy on indentation results. Moderate correlations are presented between mean indentation modulus and static (R² = 0.53) and dynamic (R² = 0.45) Young’s moduli, for Montney Formation shales. Geomechanical correlations with mineralogy suggest mean indentation results are controlled by dominant, stiff components of shale, with minimal influence from clay composition. Variability in indentation results can be applied as an index through geomechanical depth profiles.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".