Determining Bitumen, Water and Solids in Oil Sands Ore by Using Low-Field NMR
Bibliographic record
Abstract
Abstract In previous work, low-field nuclear magnetic resonance (NMR) has been considered as a fast and non-destructive method to characterize oil and water. In this work, we continue to use the low-field NMR technique to determine the amount of bitumen, water and solids for unconsolidated oil sand ores from two different depositional environments. Simple T2 cutoff and signal deconvolution are applied to the NMR spectra to estimate bitumen and water content. Comparison results are given. It has been found previously that, in most cases, the signals from clay-bound water and bitumen overlap; thus, the estimation of fluid content needs correction. To replace the well-known Dean-Stark extraction method, it is necessary to seek a fast, simple, non-destructive and inexpensive method. A densitometry technique, with simultaneous pore volume measurement, is developed to provide the volume of the ore sample and complement the NMR results. A density algorithm is introduced to determine fluid and solid content. Results from pore-volume measurements are comparable with those from Dean-Stark extraction and low-field NMR. A combined NMR-pore volume technique appears to minimize errors compared to Dean-Stark extraction. Introduction It is recognized that the global oil demand has continued to accelerate over the years and conventional oil supplies are in decline. Therefore, more attention turns to unconventional resources such as oil sands. The Alberta oil sands, the largest source of bitumen in the world, are relatively under-exploited, with estimated recoverable bitumen reserves of roughly 39 billion m3 and established reserves of approximately 376 billion m3(1). However, the varying depositional history and complex sedimentary sequences(2) result in formations hosting oil sands deposits that are not homogeneous, with significant differences in thickness of lean/rich bitumen zones, permeability, as well as bitumen and water saturation. Moreover, most of the higher-quality deposits are already producing or under development. In order to evaluate the oil sands potential resource and generate maximum economic returns, it is necessary to determine the content of bitumen, water and solids properly. Low-field nuclear magnetic resonance logging has become popular since the 1990s for analyzing reservoir fluids and fluid/rock interaction. Recently, considerable effort has been put into the applications of NMR technology in oil sands. Such work includes bitumen viscosity determination(3–6), in situ viscosity measurement(7, 8), the clay content estimation in unconsolidated samples(9–11) and in situ fluid saturation assessment for ore and froth(12, 13). In oil sands mining operations, a quick and accurate way to determine the bitumen, water and solids content would improve the operating costs efficiently. Dean-Stark extraction has been considered to be an industry standard to measure the bitumen, water and solids(14), but such an extraction procedure is highly time-consuming and requires significant volumes of chemical solvents. Also, the sample itself is totally destroyed after the extraction. Our research proposes a fast and non-invasive method. A pore volume experiment is run to determine the amount of solids using a density algorithm. The result is integrated with low-field NMR relaxometry to obtain the amount of bitumen and water. The first part of this paper describes how the low-field NMR measurement and density algorithm work on oil sands ore. This is followed by the description of the experiment workflow and the analysis for samples from two different depositional environments. The final part shows th
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".