Estimation of Petrophysical Properties of Tight Rocks from Drill Cuttings Using Image Analysis: An Integrated Laboratory-Based Approach
Bibliographic record
Abstract
Abstract Standard laboratory techniques for determining petrophysical and geomechanical properties (e.g. porosity/permeability, Young's modulus, unconfined compressive strength) of tight rocks routinely require perfectly-cylindrical core plugs and/or large amounts (30+ g) of crushed-rock materials. Further, these measurements are time-consuming and expensive to perform. Therefore, indirect approaches for estimating rock petrophysical and geomechanical properties using small amounts of drill cuttings, which are usually the only reservoir samples available from multi-fractured horizontal wells (MFHWs), have recently received attention. Using an integrated, multidisciplinary approach that combines a customized image analysis software developed in-house with non-destructive microscopic techniques, practical laboratory-based workflows are generated to determine a variety of rock petrophysical properties including mineralogical composition, cementation, and porosity using drill cuttings. An "artificial" cuttings sample suite (core plugs crushed/sieved to 20/35 US mesh size), obtained from a prolific liquid-rich tight siltstone reservoir within the Montney Formation (Alberta, Canada), is analyzed in this study. Using a scanning electron microscope (SEM), back scattered electron (BSE) images, energy-dispersive X-ray spectroscopy (EDS) and cathodoluminescence (CL) images are collected to be used as inputs for an in-house image analysis software. Elemental maps obtained from EDS allow for the distribution of the mineral assemblage to be computed. Experimental observations indicate that rock petrophysical and geomechanical properties are partly controlled by the rock microstructure/microfabric in the studied Montney samples. Using a cathodoluminescence (CL) microscope and image processing, the detrital quartz grain is resolved from surrounding cement to determine the total percentage of cement in the samples. The cement content in the sixteen samples ranges from 14.5% to 23.7%, with an average overall cement content of 19.5%. For the analyzed samples, the porosity values estimated from microscopic images ranges from 3.5% to 10.4%, averaging 5.8%. The developed algorithms for indirect estimation of mineralogical composition, cementation and porosity from drill cuttings have significant practical applications for characterization of the Montney. Performing non-destructive microscopic observations using drill cuttings, these algorithms can be used as an alternative tool to provide quantitative estimates of rock fabric/texture and reservoir quality along any vertical/lateral intervals of interest within the Montney. In absence of core plugs and/or large amounts (30+ g) of crushed-rock material, the application of this integrated workflow could be of significant interest to the Montney operators, at least at the preliminary stage of stimulation treatment, to selectively target intervals along the vertical/lateral sections of the reservoir for improving performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".