Petrophysical Considerations in Evaluating and Producing Shale Gas Resources
Bibliographic record
Abstract
Abstract We present a practical assessment of petrophysical properties of shales and their measurement in the lab and via logs. Gasbearing shale present unique measurement challenges due to their ultra-low permeability and complicated pore volume connectivity. The combination of low intrinsic permeability and gas sorption effects renders these reservoirs "unconventional". Advances in horizontal drilling and hydraulic stimulation have transformed gas-shale resources into economic reserves. Given their economic significance, there is a strong drive to understand gas shale petrophysical property measurements, both in the laboratory and in the subsurface. We note that various core analysis protocols are used in different laboratories leading to physical property measurements that are inconsistent, even when measured on identical sample sets. In addition, log analysis of kerogen-rich shale is ‘unconventional’ compared to classical techniques used in tight gas sands. As shale gas evaluation is becoming widely practiced among service companies and operators, we will focus on three reservoir assessment categories: storage capacity (gas-in-place), flow capacity (gas deliverability) and mechanical properties impacting hydraulic stimulation. Within each of these categories we have identified influential petrophysical properties such as rock composition, total organic carbon (TOC) content, porosity, saturation, permeability and mechanical properties. Specifically, we demonstrate the importance of estimating accurate mineral and kerogen content as these properties directly impact rock quality, hydraulic fracturing protocols, and gas-in-place estimations. In reviewing these practices, we also will show the need and possible direction of new technologies that will be required for making evaluations more accurate and quantitative in the future.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".