Integration of Mineralogy, Petrophysics, Geochemistry and Geomechanics to Evaluate Unconventional Shale Resources
Bibliographic record
Abstract
Abstract It is not entirely understood how related geological parameters vary during the thermal maturation and development of shale resources and controlling factors of shale productivity. Here, a detailed examination of mineralogy, geochemistry, petrophysics, and geomechanics-related data is conducted to explore the productivity of the Fox Creek, Alberta shale play. Experiments using X-Ray Diffraction, Tight Rock Analysis, Rock-Eval Pyrolysis, and Triaxial Compression are conducted to characterize the mineralogy, petrophysics, geochemistry, and geomechanics of the region under study. Multiple Linear Regression (MLR) is used to quantify the relationship between shale output productivity and reservoir input parameters. Using 300 core samples from 15 wells targeting the Duvernay shale, the key governing characteristics of shale potential were then examined. The Duvernay shale is dominated by quartz, clay, and calcite, according to X-Ray Diffraction measurements. Tight Rock Analysis indicates that the effective porosity of the Duvernay shale ranges from 1.56% to 6.11%, with an average value of 3.97 %, while the core permeability ranges from 0.25 to 345.5 nD, with an average value of 127.2nD. The total organic carbon (TOC) content ranged from 2.32 to 5.0 %, with an average of 3.86 %, according to Rock-Eval Pyrolysis testing. The majority of the Duvernay shale near the Fox Creek region (i.e., Fox Creek shale) was deposited in an oxygen-depleted maritime environment, whereas the Duvernay shale was in the gas generation window. The MLR technique determines the elements controlling shale productivity, including the production index, gas saturation, clay content, porosity, total organic carbon, brittleness index, and brittle mineral content as input parameters in decreasing order. Based on the MLR prediction model, the expected 12-month shale gas production per stage corresponds well with the actual value. This strategy can guide the future selection of horizontal well drilling sites and lead to the efficient and profitable development of shale resources.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".