Assessing Magnetic Methods for Estimating Mineral Content of Rocks in Northern Alberta
Notice bibliographique
Résumé
Historically, magnetic measurements have been crucial for paleomagnetic reconstructions, while their use in petrophysics was once considered limited. Recent studies, however, have successfully utilized magnetic susceptibility for petrophysics applications to characterize rock core samples from different types of oil and gas reservoirs, including clastic shoreface, carbonate, and unconventional reservoirs. This research aimed to apply advanced magnetic core analysis techniques, along with available borehole magnetic susceptibility data, to characterize rocks in Northern Alberta. The study explored and compared two magnetic methods for quantifying rock mineral composition and identifying lithological changes, assessing their accuracy and comparing the mineral composition estimates with results from previous whole-rock geochemistry analysis. It also examined the utility of ferrimagnetic grain domain states (e.g., for minerals such as magnetite) to identify depth-related patterns. We used powdered rock samples collected from different wells near Fort McMurray, Northern Alberta, as analogues for drill cuttings, which are a free, underutilized, and widely available resource. Magnetic measurements were performed using a portable, cost- effective low-field Bartington sensor and a large, more expensive Variable Field Translation Balance (VFTB) apparatus, which allows the application of varying magnetic field strengths, generating magnetic hysteresis curves. We simulated temperature variations observed in real boreholes during the VFTB measurements to improve estimates of mineral compositions, since magnetic properties vary with temperature and thus with depth. High-field susceptibility, determined from magnetic hysteresis curve data, was processed to quantify paramagnetic (weak positive magnetic susceptibility) and diamagnetic (weak negative magnetic susceptibility) mineral contents using two different methods: (i) a Simple Mixture Approximation method and (ii) a temperature-dependent method based on Curie's Equation. Ferrimagnetic mineral contents (likely magnetite) and domain state were estimated from the low-field segment of the hysteresis curves, and compared with the results from the Bartington sensor and some available borehole magnetic susceptibility data, offering insights on lithological changes and possible rock alteration in situ. Using the two methods mentioned above, we estimated the content of paramagnetic and diamagnetic minerals in the powdered rock samples. In most samples, paramagnetic minerals such as biotite, pyroxene, chlorite, amphibole, and garnet did not exceed 10%, while in a few cases they were larger, up to around 40%. The remaining minerals were diamagnetic, including quartz and feldspar. The results showed average mineral content uncertainty is ±0.03% for the Simple Mixture Approximation method and ±0.02% for Curie's Equation estimations, and exhibited good agreement with previous geochemical analysis, confirming the reliability of the magnetic techniques. Even without prior knowledge of rock composition, these methods can be effectively applied using educated assumptions about mineral components. Hysteresis curve analysis from the VFTB results enabled quantitative characterization of the ferrimagnetic mineral fraction, likely magnetite, revealing variations in its content with depth. We also observed variations in superparamagnetic, stable single domain, pseudo-single domain, and multidomain behavior of magnetite particles with depth. Magnetite content estimated via the VFTB, Bartington sensor and high-resolution borehole magnetic susceptibility data all revealed an increase in magnetite content with depth. These observations indicated that the more affordable, portable Bartington sensor has a potential advantage for producing rapid results on drill cuttings and other core samples, and borehole magnetic susceptibility measurements can be useful to estimate lithological trends and rock alterations in-situ. The temperature dependent magnetic results measured on the powdered rock samples can be used to improve estimations of mineral contents with depth from borehole magnetic susceptibility data. Overall, this research highlights the potential of magnetic techniques as rapid, non-
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».