Hyperspectral fluorescence imaging: Robust detection of petroleum in porous sedimentary rock formations
Bibliographic record
Abstract
Abstract Examining hand samples can be a necessary step for geologic studies, and effective mapping of such samples can be achieved through the high spectral and spatial resolutions of ground-based hyperspectral imaging (HSI) at the millimeter to centimeter scale. We have developed a simple approach to crude oil identification and characterization — feasible in 16 h — based on hyperspectral data collected under ultraviolet (UV) lighting and normalized with respect to the fluorescence patterns of the Spectralon diffuse reflectance material. The samples under consideration were extracted from a core acquired from an Early Cretaceous bituminous sandstone formation in the Athabasca Basin located near Fort McMurray, Alberta, Canada. This basin contains the largest natural bitumen deposit in the world, where surface mining operations currently are viable only for approximately 20% of the estimated 164 billion barrels of total recoverable oil reserves. This deposit is unique in that its tar sands are water-wet, which facilitates the separation of bitumen from the sandstone via water-based gravity separation. However, large amounts of water are still required for oil recovery; therefore, a fast and reliable way to mark portions of the deposit where ample petroleum has accumulated and assess its extractability based on its physical characteristics prior to mining can be helpful for optimizing resource usage. For this reason, we test and visually develop the ability of three classification methods — the spectral angle mapper, support vector machine, and supervised neural network — to distinguish among bitumen, Spectralon, and a nonfluorescent slate background based on the emission of visible light in response to absorbing UV light of different wavelengths. We also adopt spectral indices useful for indicating concentrated bitumen in tar sands. Errors inherent to the methodology are discussed along with ways to mitigate them. After accounting for these, HSI can be a valuable asset alongside other techniques used for production economics evaluation.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".