Gas Shale Characterization - Results of the Mineralogical, Lithological and Geochemical Analysis of Cuttings Samples from Radioactive Silurian Shales of a Palaeozoic Basin, SW Algeria
Bibliographic record
Abstract
Abstract The successful recovery of hydrocarbons from shales is dependent on physical rock properties such as lamination, brittleness and the presence of natural fractures, as well as chemo-physical properties such as absorption and adsorption. The key parameters defining the hydrocarbon potential of shales are the mineralogy, organic carbon content (TOC) and burial history. These parameters are commonly derived by conventional and special core analysis of SWCs or cores. This study highlights that most of the analyses and techniques used to determine the hydrocarbon potential of shales can also be performed on cuttings material with a high degree of confidence. The usage of cutting material has significant advantages with respect to sample availability, sample coverage and acquisition costs. Accurate depth allocation of cuttings is problematic but, by careful reference of the measured mineralogical and textural features to the available mudlog and wireline data, the effects can be minimised. As part of a research project, SGS analysed the mineralogical and lithological composition of 80 cuttings samples, from various wells in the Radioactive Silurian Shale (drilled a Palaezoic Basin, SW of Algeria) by QEMSCAN. In addition, the geochemistry and maturity parameters were determined by Leco (TOC) and RockEval (S1-S3) methods. Furthermore high resolution 3D computer tomography scans (CT) were performed in order to analyse the cutting material for lamination and micro-fractures. The compilation and interpretation of the mineralogical, lithological and geochemical data indicate significant vertical and lateral heterogeneities, probably induced by local facies changes, which possibly lead to strong variances in the shale gas potential. The properties of the Silurian hot shale samples were compared with QEMSCAN results of shale samples from Canada, UK, published data from the US gas shales and source rocks from Netherlands and Sweden. Based on the comparison, a distinct trend could be established, indicating significant differences in mineral composition between producing gas shales and other more "conventional" gas source rocks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".