Clarifying the Contribution of Clay Bound Water and Heavy Oil to NMR Spectra of Unconsolidated Samples
Bibliographic record
Abstract
Abstract Low-field nuclear magnetic resonance (NMR), whether implemented in a logging tool, bench top analyser or on-line sensor, cannot detect the complete response of heavy oil or bitumen. Both heavy oil and bitumen relax quickly so the majority of these oils' spectra are detected at relaxation times less than 10 ms at room temperature. In clay-free sands, the contribution of heavy oil to the NMR spectrum is distinct and, as a result, it is still possible to calculate oil and water content based on NMR spectra. However, in sands that contain clays, the relaxation times of clay bound water are in the same range as bitumen. Experimental results from mixtures containing illite, kaolinite, montmorillonite, sand and mild brine show that clay bound water has a characteristic response. These NMR ‘signatures’ were used to develop predictive nomographs of clay content. A second set of experiments involved adding heavy oil to mixtures containing clay, sand and brine. The changes in NMR spectra after exposure to heavy oil were compared to the spectra obtained before oil was added. The differences identified in this work allowed for improvements in calculating water, oil and/or solids content. This paper presents a preliminary predictive algorithm for clay content determination, and this knowledge will allow one to more accurately separate the contributions of heavy oil and clay bound water from a sample despite the fact that these will overlap in an NMR spectrum. Improved characterization of oil sands is a possible consequence of this work. Introduction Nuclear magnetic resonance (NMR) logging tools have been used in numerous applications within the petroleum industry for enhancing recovery. In addition to porosity and permeability determination, NMR has been used to characterize heavy oil and bitumen(1, 2), composition determination of oil/water emulsions(3) and determination of heavy oil viscosity(4, 5). NMR logging tools obtain information regarding fluids in porous media by using magnetic fields to polarize the protons in the fluid and by monitoring the time it takes the protons to return to equilibrium. This time is commonly termed the transverse relaxation time (T2). Protons in bulk fluids such as water have a T2 value of approximately two seconds, but the T2 values for heavy oils and bitumen are much faster (e.g. between 1 and 10 ms). The reason for this is that the protons in heavy oil and bitumen are restricted due to the viscous environment(6). In fact, present NMR logging tools are incapable of detecting the complete spectrum from heavy oil and bitumen formations because of the high viscosities of these samples. As a result, attempts to characterize heavy oil and bitumen are problematic. Restriction of proton movement can also occur because the fluid has sorbed onto clays or organic matter in the sample(7). Consequently, clay bound water has a low T2 value (e.g. less than 10 ms) compared to water residing in the larger pores of the samples, which has a T2 value that is approximately 100 ms. The fact that the amplitude peaks for heavy oil and clay bound water appear at similar T2 values makes it difficult to differentiate between the oil and water signals from a sample that contains both fluids(8).
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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".