LOW-FIELD TIME-DOMAIN NMR MEASUREMENT OF OIL SANDS PROCESS STREAMS

Abstract

A method for determining the solids content, fines content and/or particle size distribution of the solids in an oil sands process stream test sample comprising bitumen, solids and water using low-field time-domain NMR is provided which involves building a non-solids partial least squares calibration model using oil sands process streams calibration samples having a known bitumen content, solids content, water content, fines content and/or particle size distribution by subjecting the calibration samples to a first T.sub.1-weighted T.sub.2 measurement NMR pulse sequence that maximizes very fast relaxing signals and a second T1-weighted T2 measurement NMR pulse sequence that maximizes slow relaxing signals. The measurement of other sample properties strongly correlated with surface area, such as methylene blue index, can also be measured using a partial least squares calibration model.

Claims

1. A method for determining the solids content, fines content and/or particle size distribution in an oil sands process stream test sample comprising bitumen, solids and water using low-field time-domain NMR, comprising: building partial least squares calibration models for non-solids content and fine particles content(s) less than a given, or multiple, particle size(s) using oil sands process streams calibration samples having a known bitumen, water, and solids contents, and a known particle size distribution of the solids, by subjecting the calibration samples to a first T.sub.1-weighted T.sub.2 NMR pulse sequence that measures fast relaxing signals and a second T.sub.1-weighted T.sub.2 NMR pulse sequence that measures slow relaxing signals, based on the shift to faster water signal relaxation times as the ratio of fine particles to water in the sample is increased; subjecting the test sample to either the first fast relaxing T.sub.1-weighted T.sub.2 NMR pulse sequence, the second slow relaxing T.sub.1-weighted T.sub.2 NMR pulse sequence, or both, and measuring the produced signal amplitudes; determining the fine particles content and/or particle size distribution in the test sample by applying the calibration loading(s) for fine particles less than a given, or multiple, particle size(s) to the fast relaxing pulse sequence raw NMR data; and/or determining the solids content by applying the non-solids loading to the slow relaxing pulse sequence raw NMR data and calculating the solids content by difference from the sample weight.

2. The method as claimed in claim 1, wherein the first T.sub.1-weighted T.sub.2 measurement NMR pulse sequence (FPS) is such that there are 15 transverse relaxation echoes spaced 0.4 ms apart, acquired at 28 T.sub.1 points, exponentially spread from 5 ms through 200 ms, and the final stretch of T.sub.2 measurement comprises 50 echoes spaced 2 ms apart, with 24 scans averaged together to improve the signal to noise ratio, resulting in the FPS measurement time of less than 1 minute and the second T.sub.1-weighted T.sub.2 measurement NMR pulse sequence (SPS) is such that there are 200 transverse relaxation echoes spaced 0.6 ms apart, acquired at 11 T.sub.1 points, exponentially spread from 5 ms through 20000 ms, and the final stretch of T.sub.2 measurement comprises 200 echoes spaced 20 ms apart, with 4 scans averaged together to improve the signal to noise ratio, resulting in the SPS measurement time of less than 3 minutes.

3. The method of claim 1, wherein the bitumen, solids and water contents of the oil sands process streams calibration samples are determined by Dean-Stark extraction.

4. The method of claim 1, wherein the particle size distribution of the oil sands process streams calibration samples are determined by laser diffraction or wet sieve.

5. The method as claimed in claim 1, wherein the particle size distribution is for particles having diameters <44 microns, <5.5 microns, and/or <1.9 microns.

6. The method as claimed in claim 5, wherein the content of fines at each size can be reported as a percentage of the whole sample or as a percentage of the solids.

7. The method as claimed in claim 1, wherein the test sample is an oil sands process sample or oil sands tailings treatment sample containing solids in the range of 0-80% by weight.

8. The method as claimed in claim 1, wherein the test sample is first preheated to the temperature of the NMR probe.

9. The method as claimed in claim 1, wherein the oil sands process stream sample is a sample from a tailings treatment process for optimizing oil sand tailings reclamation.

10. The method as claimed in claim 1, wherein the oil sands process stream sample is a sample from the bitumen extraction process for optimizing the recovery of bitumen.

11. The method as claimed in claim 1, wherein a methylene blue index (MBI) value for a sample is measured using the same approach described for the measurement of fine particle content in the sample by TD-NMR using the FPS raw NMR data sets, except where the PLS calibration reference values are in units of total MB milliequivalents in the sample container.

12. A method for determining the solids content, fines content and/or particle size distribution in an oil sands process stream test sample comprising bitumen, water, and solids using low-field time-domain NMR, comprising: (a) initially saturating the magnetization of the sample so that essentially no magnetization remains in the +Z axis by applying 10 rapid 90° radio-frequency (RF) pulses to the sample prior to each T.sub.1-weighted T.sub.2 measurement; (b) subjecting the sample to either a first combined recovery and transverse relaxation sequence of NMR radio-frequency pulses comprising a T.sub.1-weighted T.sub.2 measurement with an emphasis on measuring faster relaxing components within the sample, a second combined recovery and transverse relaxation sequence of NMR radio-frequency pulses comprising a T.sub.1-weighted T.sub.2 measurement with an emphasis on measuring slower relaxing components within the sample, or both; (c) recording the signal amplitudes from the transverse relaxation (T.sub.2) echo trains after incremental longitudinal relaxation (T.sub.1) to produce a raw TD-NMR data set that emphasizes faster relaxing components within the sample; (d) providing a computer which has been programmed to determine the amount of solids, fines and/or particles less than a given particle size in the sample by means of an optimized partial least squares chemometric model relating (i) the faster relaxing raw TD-NMR data sets obtained from a training set of oil sand process samples to the training samples' corresponding reference values obtained from analysis methods for determining bitumen, fine solids less than a given particle size, and/or the particle size distribution of the solids, and (ii) relating the slower relaxing raw TD-NMR data sets obtained from a training set of oil sand process samples to the training samples' corresponding reference values obtained from analysis methods for determining water and non-solids, and using the sample weight to determine the solids content by difference from the non-solids result.

13. The method as claimed in claim 12, wherein the first pulse sequence (FPS) of radio-frequency pulses is such that there are 15 transverse relaxation echoes spaced 0.4 ms apart, acquired at 28 T.sub.1 points, exponentially spread from 5 ms through 200 ms, and the final stretch of T.sub.2 measurement comprises 50 echoes spaced 2 ms apart, with 24 scans averaged together to improve the signal to noise ratio, resulting in the FPS measurement time of less than 1 minute and the second pulse sequence (SPS) of radio-frequency pulses is such that there are 200 transverse relaxation echoes spaced 0.6 ms apart, acquired at 11 T.sub.1 points, exponentially spread from 5 ms through 20000 ms, and the final stretch of T.sub.2 measurement comprises 200 echoes spaced 20 ms apart, with 4 scans averaged together to improve the signal to noise ratio, resulting in the SPS measurement time of less than 3 minutes.

14. The method of claim 12, wherein one of the reference analysis method is Dean-Stark extraction for measuring bitumen, water, and solids in the training samples.

15. The method of claim 12, wherein one of the reference analysis methods is laser diffraction or wet sieve for measuring the particle size distribution of the solids in the training samples.

16. The method as claimed in claim 12, wherein the measured fine particles can have diameters <44 microns, <5.5 microns, and <1.9 microns as measured by the reference analysis method.

17. The method as claimed in claim 16, wherein the content of fines particles at each size can be reported as a percentage of the whole sample or as a percentage of the solids.

18. The method as claimed in claim 12, wherein the test sample is an oil sands process sample or oil sands tailings treatment sample containing solids in the range of 0-80% by weight.

19. The method as claimed in claim 12, wherein the test sample is preheated to the temperature of the NMR probe.

20. The method as claimed in claim 12, wherein a methylene blue index (MBI) value for a sample is measured using the same approach described for the measurement of fine particle content in the sample by TD-NMR using the FPS raw NMR data sets, except where the PLS calibration reference values are in units of total MB milliequivalents in the sample container.

21. The method as claimed in claim 12, wherein the oil sands process stream sample is a sample from a tailings treatment process for use in optimizing oil sand tailings reclamation.

22. The method as claimed in claim 12, wherein the oil sands process stream sample is a sample from the bitumen extraction process for use in optimizing the recovery of bitumen.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0029] FIG. 1 shows the first pulse sequence (FPS) raw NMR signal for a fluid fine tailings centrifuge feed sample (˜20% solids) plotted versus time.

[0030] FIG. 2 shows the same data shown in FIG. 1 plotted versus data point number.

[0031] FIG. 3 shows the FPS raw NMR signal for a centrifuge cake sample (˜50% solids).

[0032] FIG. 4 shows the FPS raw NMR signal for a centrifuge centrate sample (˜2% solids).

[0033] FIG. 5 shows the FPS signal for a mixture of coarse tailings sand and process water (˜50% solids).

[0034] FIG. 6 shows the FPS raw NMR signal for pure bitumen.

[0035] FIG. 7 shows the second pulse sequence (SPS) raw NMR signal for a fluid fine tailings centrifuge feed sample plotted versus time.

[0036] FIG. 8 shows the same data shown in FIG. 7 plotted versus data point number.

[0037] FIG. 9 shows the SPS raw NMR signal for a centrifuge cake sample.

[0038] FIG. 10 shows the SPS raw NMR signal for a centrifuge centrate sample.

[0039] FIG. 11 shows the SPS raw NMR signal for a mixture of coarse tailings sand and process water.

[0040] FIG. 12 shows the SPS raw NMR signal for pure bitumen.

[0041] FIG. 13 shows the PLS model loading for determining the amount of fine particles (<44 micron) in the sample from the FPS raw NMR data.

[0042] FIG. 14 shows the PLS model loading for determining the amount of non-solids in the sample from the SPS raw NMR data.

[0043] FIG. 15 shows the % bitumen agreement for test set samples measured by NMR using the first pulse sequence (FPS) compared to % bitumen measured by Dean & Stark.

[0044] FIG. 16 shows the agreement with Dean & Stark measurements obtained for test set samples when % water is measured using the second pulse sequence (SPS).

[0045] FIG. 17 shows the agreement obtained for test set samples between the % solids measured by the NMR based on the second pulse sequence (SPS) non-solids calibration model of the present invention and the reference % solids measured by Dean & Stark.

[0046] FIG. 18 shows that the agreement obtained by calculating the solids by difference (100%−% bitumen−% water).

[0047] FIG. 19 shows the agreement for test set samples between the FPS model % <44 micron particles in the sample and the reference values.

[0048] FIG. 20 shows the agreement of FIG. 19 when the samples with >2.5% bitumen are removed from both the calibration set and test set.

[0049] FIG. 21 shows the agreement for the % <5.5 micron particles content using the FPS model.

[0050] FIG. 22 shows the agreement of FIG. 21 when the samples with >2.5% bitumen are removed from the calibration set and test set.

[0051] FIG. 23 shows the agreement for the % <1.9 micron particles content using the FPS model.

[0052] FIG. 24 shows the agreement of FIG. 23 when the samples with >2.5% bitumen are removed from the calibration set and test set.

[0053] FIG. 25 shows the agreement for the Methylene Blue Index using a FPS model.

[0054] FIG. 26 shows a flow chart for one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0055] The present invention uses low-field time-domain nuclear magnetic resonance (low-field TD-NMR) instruments to analyze various oil sands process streams to provide an accurate measurement of total solids and fine particles in addition to bitumen and water. Some significant advantages of using low-field TD-NMR measurements are that the measurements are quick, non-destructive, simple to use, and as a result, are less susceptible to technician bias. They typically require no solvents, gases, plumbing, or special ventilation, only electricity and a clean, temperature-controlled environment.

[0056] The present invention can be used with a variety of oil sands process streams, for example, oil sand slurry, extraction tailings including middlings and coarse tailings, fluid fine tailings (FFT) from settling basins, diluted FFT centrifuge feed, centrifuge cake, centrifuge centrate, and composite tailings feed and product streams.

[0057] In the examples that follow, low-field TD-NMR measurements were performed using a LF65 NMR instrument (Bruker BioSpin Ltd, Milton, ON, CAN). Two T.sub.1-weighted T.sub.2 measurement NMR pulse sequences performed in series were used to collect two separate groups of raw TD-NMR data sets. Partial least squares calibration models were created using the raw TD-NMR data sets from oil sands process streams having a known amount of solids, fine particles with diameters less than various sizes, bitumen, and water, (hereinafter referred to as “calibration samples”). The first T.sub.1-weighted T.sub.2 NMR pulse sequence measurement (FPS) was developed to emphasize the recording of the faster relaxing components within the sample (e.g. bitumen and water associated with fine particles). The second T.sub.1-weighted T.sub.2 NMR pulse sequence measurement (SPS) was developed to emphasize the recording of slower relaxing components in the sample (e.g. bulk water). For every calibration sample analyzed, both pulse sequences were automatically applied and the raw data stored as separate files. It was discovered that the combined use of the two different pulse sequences allows for more accurate predictions of the composition of oil sand process stream samples as compared to using a single pulse sequence. This is especially true when measuring both the solids content and fine particle content of samples that vary over a wide range of compositions (e.g. 2% versus 60% solids). Thus, the approach taken in the present invention allows for both fast and accurate measurements that were previously not possible.

Example 1

[0058] In total, 206 oil sands process stream samples were used in this example. Two-thirds of the samples were used to construct the partial least squares (PLS) calibration models (i.e., calibration samples) and the resulting models were used to predict the composition of the remaining ⅓ of the samples (hereinafter referred to as the “test samples”). The oil sands process streams collected were predominantly from a centrifuge commercial demonstration plant used to centrifuge flocculant-treated oil sands tailings. However, to provide some additional sample diversity (including diversity of the minerals and particle size distribution), a number of other oil sands process streams were collected from various oil sands extraction plants. A few pure bitumen samples were also used to better differentiate between the NMR signal due to bitumen and the NMR signal due to water associated with fine particles. The samples were collected in either glass jars with plastic lids or high density polyethylene plastic bottles up to 250 mL in size. Using the same containers for the calibration samples and test set samples produced the most accurate results.

[0059] Prior to measurement in the LF65 NMR instrument, the samples were heated to 37° C. for at least 1 hour in a FREAS 625S convection oven (Thermo Fisher Scientific, Waltham, Mass., USA) to match the internal NMR probe temperature. Minimum sample heating times should be determined for a given oven and starting sample temperature. Sample weight and container material may also impact the minimum sample heating time. Once the sample was removed from the oven, it was quickly shaken by hand to briefly re-suspend any settled solids and then manually placed into the LF65 NMR probe cavity. Following the ˜4 minute NMR analysis time, the sample was removed, and the process repeated for the next sample.

[0060] FIG. 1 shows the first pulse sequence (FPS) raw NMR signal for a fluid fine tailings centrifuge feed sample (˜20% solids) plotted versus time. FIG. 2 shows the same data plotted versus data point number, which makes it easier to visualize the data. FIG. 3 shows the FPS raw NMR signal for a centrifuge cake sample (˜50% solids). FIG. 4 shows the FPS raw NMR signal for a centrifuge centrate sample (˜2% solids). FIG. 5 shows the FPS signal for a mixture of coarse tailings sand and process water (˜50% solids). The fastest T.sub.1 weighted T.sub.2 relaxation behavior is observed for the centrifuge cake sample (largest ratio of fine particles to water). The slowest relaxation behavior is observed for the coarse tailings sand and process water sample (smallest ratio of fine particles to water). FIG. 6 shows the FPS raw NMR signal for pure bitumen, which undergoes very fast relaxation.

[0061] The differences in the T.sub.1 and T.sub.2 relaxation behavior as measured by the FPS parameters between bitumen and water associated with solids of different particle sizes, mineral compositions (e.g. quartz, various clays), and at different ratios of solids-to-water, can be exploited using chemometrics to measure different components of interest that are associated with relatively fast relaxation, such as the concentration of fine particles in the sample <44 microns, <5.5 microns, <1.9 micron, and % bitumen.

[0062] FIG. 7 shows the second pulse sequence (SPS) raw NMR signal for a fluid fine tailings centrifuge feed sample plotted versus time. FIG. 8 shows the same data plotted versus data point number. FIG. 9 shows the SPS raw NMR signal for a centrifuge cake sample. FIG. 10 shows the SPS raw NMR signal for a centrifuge centrate sample. The SPS parameters have been selected to observe the relaxation behavior of slower relaxing components in the sample, such as water associated with smaller amounts of fine particles. FIG. 11 shows the SPS raw NMR signal for a mixture of coarse tailings sand and process water. FIG. 12 shows the SPS raw NMR signal for pure bitumen, which relaxes so quickly that only small amounts of signal are observed. However, sufficient bitumen signal is still collected under the SPS parameters to contribute to an accurate non-solids PLS calibration model.

[0063] Reference % bitumen, % water, and % solids results were provided by Dean-Stark extraction. Reference particle size information for the clean and dry solids produced by the Dean-Stark extraction was obtained by an LS 13 320 laser diffraction instrument (Beckman Coulter Inc., Brea, Calif., USA).

[0064] Chemometric reference results were calculated based on the grams of a component of interest in the sample container. Because the NMR produces minimal signal for solids, non-solids content reference values were used to develop a calibration that could accurately predict the solids content by difference. For example, a 100 grams tailings sample found to contain 30% solids, 68% water, and 2% bitumen by Dean & Stark (of whole sample), and 90% <44 micron, 50% <5.5 micron, and 20% <1.9 micron by Coulter laser diffraction (of solids fraction) would have reference values of (70% non-solids)×(100 g)=70 g non-solids; (68% water)×(100 g)=68 g water; (2% bitumen)×(100 g)=2 g bitumen; (90% <44 micron)×(30 g solids)=27 g<44 micron content; (50% <5.5 micron)×(30 g solids)=15 g<5.5 micron content; (20% <1.9 micron)×(30 g non-solids)=6 g<1.9 micron content. In the rare event that the NMR produces a slightly negative value as a test set prediction (e.g. −0.05% solids), a value of zero is returned instead.

[0065] Separate chemometric PLS models for each component of interest were built using OPUS software version 7.0129 (Bruker BioSpin Ltd, Milton, ON, CAN). The regions of the raw NMR spectra that were used to build each PLS model were selected using the built-in optimization routine within the OPUS software. FIG. 13 shows the PLS model loading for determining the amount of fine particles (<44 micron) in the sample from the FPS raw NMR data. FIG. 14 shows the PLS model loading for determining the amount of non-solids in the sample from the SPS raw NMR data.

% Bitumen Content

[0066] FIG. 15 shows the % bitumen agreement for test set samples measured by NMR using the first pulse sequence (FPS) compared to % bitumen measured by Dean & Stark. Table 1 shows the average difference between the NMR and the Dean & Stark method, the standard deviation of the differences, the maximum absolute difference, as well as the R-squared value of the correlation. The average difference is very low, while the standard deviation of the differences, maximum difference, and R-squared value provide numeric indications of the magnitude of scatter.

TABLE-US-00001 TABLE 1 Average, standard deviation, maximum differences, and R.sup.2 between the NMR first pulse sequence (FPS) bitumen model and the reference bitumen. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % Bitumen −0.01 0.31 0.94 0.9425 (FPS model)

[0067] It should be noted that there was poorer agreement between % bitumen predicted using the second pulse sequence (SPS) bitumen PLS model and Dean & Stark. This is expected given that the second pulse sequence primarily collects NMR signal at long relaxation times, where the bitumen signal has largely already relaxed. This makes it difficult to differentiate the small amount of SPS bitumen signal from the overlapping water signal, which is why the FPS is much better for the measurement of bitumen.

% Water Content

[0068] FIG. 16 shows that excellent agreement with Dean & Stark measurements is obtained for test set samples when % water is measured using the second pulse sequence (SPS). Poorer agreement was obtained when the first pulse sequence (FPS) was used to measure the water content. This is also expected as the FPS does not collect enough signal at long relaxation times, where the water signal for samples with relatively few fines particles can be observed (e.g. centrate, coarse sand and water). Table 2 shows the same numeric indicators of the agreement for the % water measurements.

TABLE-US-00002 TABLE 2 Average, standard deviation, maximum differences, and R.sup.2 between the NMR second pulse sequence (SPS) water model and the reference water. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % Water (SPS model) 0.14 0.64 1.8 0.9993

% Solids Content

[0069] FIG. 17 shows that excellent agreement is obtained for test set samples between the % solids measured by the NMR based on the second pulse sequence (SPS) non-solids calibration model of the present invention and the reference % solids measured by Dean & Stark. FIG. 18 shows that good agreement can also be obtained by calculating the solids by difference (100%−% bitumen−% water), although not as good as shown in FIG. 17 and not sufficiently good to measure the % solids in typical centrate samples shown in the magnified section of the graph. Table 3 shows how the SPS non-solids model % solids agreement with Dean-Stark compares to the by-difference % solids agreement with Dean-Stark based on a variety of numeric indicators.

TABLE-US-00003 TABLE 3 Average, standard deviation, maximum differences, and R.sup.2 between the NMR solids content and the reference solids content. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % Solids (non-solids 0.09 0.48 1.5 0.9996 SPS model) % Solids (100% - % 0.14 0.51 1.7 0.9995 bitumen by FPS - % water by SPS)

Amount of Fine Particles in the Sample

[0070] The amount of fine particles within the test samples are expressed as a percent of the entire sample (i.e. % <44 micron, % <5.5 micron, and % <1.9 micron of the sample), rather than as a percentage of the solids fraction. This is done because the amount of fine particles in the whole sample rather than as a percentage of the solids can be a more useful parameter for determining optimal process aid dosage and tracking fine particles.

[0071] For measuring the amount of fine particles in the sample, the first pulse sequence (FPS) models produced better agreement with the reference values compared to the second pulse sequence (SPS) models. This is because the FPS parameters emphasize the recording of the faster relaxing components, such as water associated with a relatively high amount of fine particles (e.g. FFT feed, centrifuge cake).

[0072] FIG. 19 shows the agreement for test set samples between the FPS model % <44 micron particles in the sample and the reference values. The agreement shows good average agreement with some scatter. Some of the scatter is partly due to the signal overlap between bitumen and water associated with high amounts of very fine particles. FIG. 20 shows how the agreement improves once the samples with >2.5% bitumen are removed from both the calibration set and test set. Table 4 shows the numerical indicators of the agreement for the % <44 micron particle content based on the FPS models,

TABLE-US-00004 TABLE 4 Average, standard deviation, maximum differences, and R.sup.2 between the NMR first pulse sequence (FPS) % <44 micron particle content in the sample and the reference results. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % <44 micron (FPS 0.43 4.8 16.0 0.9439 model) % <44 micron (FPS 0.06 1.4 3.5 0.9957 model with <2.5% bitumen calibration and test set samples only)

[0073] FIG. 21 shows the agreement for the % <5.5 micron particles content using the FPS model, while FIG. 22 shows the same agreement, except with all of the samples with >2.5% bitumen removed from the calibration set and test set. Again, the removal of the high bitumen samples greatly improves the agreement. Table 5 shows the numerical indicators of the agreement for the % <5.5 micron particle content based on the FPS models.

TABLE-US-00005 TABLE 5 Average, standard deviation, maximum differences, and R.sup.2 between the NMR first pulse sequence (FPS) % <5.5 micron particle content in the sample and the reference results. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % <5.5 micron (FPS 0.53 2.1 7.8 0.9703 model) % <5.5 micron (FPS 0.18 1.0 2.6 0.9940 model with <2.5% bitumen calibration and test set samples only)

[0074] FIG. 23 shows the agreement for the % <1.9 micron particles content using the FPS model, while FIG. 24 shows the same agreement, except with all of the samples with >2.5% bitumen removed from the calibration set and test set. Again, the removal of the high bitumen samples greatly improves the agreement. Table 6 shows the numerical indicators of the agreement for the % <1.9 micron particle content based on the FPS models.

TABLE-US-00006 TABLE 6 Average, standard deviation, maximum differences, and R.sup.2 between the NMR first pulse sequence (FPS) % <1.9 micron particle content in the sample and the reference results. Average Difference Std Dev of Maximum NMR - Difference Absolute Reference (% Difference Component (% Absolute) Absolute) (% Absolute) R.sup.2 % <1.9 micron (FPS 0.14 0.94 3.1 0.9759 model) % <1.9 micron (FPS 0.15 0.63 2.2 0.9904 model with <2.5% bitumen calibration and test set samples only)

[0075] Thus, the ability of the NMR to predict the amount of fine particles in the sample at various particle sizes in just over a 1 hour turnaround time (sample heating time is 1 hour and the NMR analysis time is ˜4 minutes) has been demonstrated.

Example 2

[0076] Three centrifuged tailings samples (FFT feed, centrate, and centrifuge cake) were analyzed 5 times each by one lab technician and then another 5 times each by a different lab technician to determine if differences in the way the samples are shaken by hand and introduced into the NMR instrument affects the results. Table 7 shows the results of this repeatability test. Overall, both the repeatability and agreement between the two sets of results produced by the different lab technicians are excellent.

[0077] Due to the scatter in the agreement in the % fines prediction (for example, see FIG. 19), the NMR can sometimes predict that there are slightly higher % >44 micron fines content in the sample, than there are total % solids. This was observed for an FFT feed sample (e.g. % solids of 25.2% versus % <44 micron fines content of 27.2%). Since the agreement with the reference method is much better for the % solids measurement compared to the fine particle measurements, one way to deal with this discrepancy is to automatically report the % <44 micron particle content as being equal to the % solids when this situation occurs.

TABLE-US-00007 TABLE 7 Repeatability of 3 centrifuged tailings samples analyzed times 5 each by 2 different Lab Technicians. Note that the % Bitumen, % Water, and % Solids (non-solids model) results were normalized to add to 100%. The % < 44, % < 5.5, and % < 1.9 results are based on the entire sample weight. Centrate Centrate FFT Feed FFT Feed Cake Cake Sample Sample Sample Sample Sample Sample Average Std Dev Average Std Dev Average Std Dev (% Absolute) (% Absolute) (% Absolute) (% Absolute) (% Absolute) (% Absolute) % Bitumen 0.77 0.13 1.61 0.03 2.68 0.05 (Tech #1) % Bitumen 0.92 0.23 1.60 0.04 2.67 0.04 (Tech #2) % Water 94.50 0.18 73.12 0.20 46.50 0.12 (Tech #1) % Water 94.11 0.30 73.21 0.13 46.57 0.02 (Tech #2) % Solids 4.73 0.07 25.27 0.21 50.82 0.14 (Tech #1) % Solids 4.96 0.17 25.20 0.16 50.76 0.03 (Tech #2) % < 44 micron 4.04 0.13 27.17 0.45 43.16 0.11 (Tech #1) % < 44 micron 4.23 0.29 27.19 0.48 43.24 0.04 (Tech #2) % < 5.5 micron 2.06 0.08 17.94 0.20 23.29 0.05 (Tech #1) % < 5.5 micron 2.03 0.06 17.94 0.19 23.34 0.01 (Tech #2) % < 1.9 micron 0.99 0.12 8.54 0.06 14.11 0.03 (Tech #1) % < 1.9 micron 0.90 0.10 8.68 0.12 14.14 0.02 (Tech #2)

Example 3

[0078] Other useful parameters of oil sand process samples can also be measured using the approach described herein, provided that the parameter being measured is strongly correlated to the surface area of the solids associated with water in the sample. The methylene blue index (MBI) titration method, based on ASTM C837-09 or one of several related procedures, is a commonly used measure of clay cation-exchange activity (often reported in units of MB milliequivalents per 100 g of clean and dry solids) that can be measured using a similar approach as described herein for measuring the amount of fine particles by TD-NMR.

[0079] In this case, the PLS calibration reference values should be calculated as the MB milliequivalents within the entire sample. This can be calculated by multiplying the MBI results for the calibration sample (e.g. in units of meq/100 g of clean and dry solids produced by Dean-Stark extraction) by the % solids by Dean-Stark extraction, and then multiplying by the sample weight to obtain a reference value in units of meq in the entire sample container. For example, the PLS calibration reference value for a sample with 12 MB meq/100 g dry solids, 20% solids, and a sample weight of 120 g would be (12 MB meq/100 g solids)×(20 g solids/100 g sample)×(120 g sample)=2.88 MB meq in the sample. A PLS model is then created using these reference values and the FPS raw NMR data for a set of calibration samples, followed by applying the built-in optimization routine with the OPUS software to select the most useful regions of the raw NMR spectra for measuring MBI values. The MBI PLS loading can then be used to determine the MB meq in an unknown test sample from its FPS raw NMR data. Using the % solids determined by the non-solids NMR model and the sample weight of the test sample, the MBI value can be reported in units of MB meq/100 g of dry solids.

[0080] This embodiment was demonstrated using 19 composite (non-segregating) tailings samples. These samples were analyzed by TD-NMR as described above to determine MBI (meq/100 g) using a FPS model and % solids using a non-solids SPS model. FIG. 25 shows the agreement for the cross validation MBI results on all 19 samples based on the FPS MBI model compared to the reference MBI values. Table 8 shows the numerical indicators of the agreement for the NMR MBI results compared with the reference values.

TABLE-US-00008 TABLE 8 Average, standard deviation, maximum differences, and R.sup.2 between the NMR first pulse sequence (FPS) Methylene Blue Index in the sample and the reference results. Average Difference Maximum NMR - Std Dev of Absolute Component Reference Difference Difference R.sup.2 MBI (meq/100 g) 0.003 0.10 0.21 0.9418