New Approach for Determination of Bitumen and Water Contents in Tar Sands
Bibliographic record
Abstract
Abstract A new interpretive algorithm has been developed for bitumen spectra analysis in tar sands. This algorithm utilizes Low Field NMR method for measurements. With the use of appropriate basic components spectra database the algorithm extracts information on bitumen contents, immovable and movable water saturations. The basic components spectra database can be built by analysis of spectra for similar types of rock or by analyzing representative set of samples from the field under study. Representative set of bitumen spectra in the bulk is also included into this database. Application of methods of multivariate analysis made it possible to differentiate between samples coming from different geological formations without any a priori knowledge about these formations. The algorithm has been tested by comparison with the results of direct measurements of bitumen/water contents carried out on an exam set of 30 samples. All the NMR results are within the measurement uncertainty of the direct measurements. Having an advantage of rapidity of processing with high reliability and accuracy this non- destructive NMRbased approach can be a vital alternative to standard direct measurements (solvent extraction) in situations, where the above-mentioned qualities of the method are crucial. Introduction The oil reserves presented by heavy oils and bitumen are accounted for roughly 6 trillions barrels, accumulated in oil bearing deposits around the world. The majority of these reserves are lumped in Canada, the Former Soviet Union, Venezuela and Nigeria2. The recent five years developments in NMR research offer number of methodological approaches and formal lgorithms for laboratory and field NMR applications used for bitumen/water contents characterization in bulk volume (stable-emultions2,3) and in porous media (laboratory and in-situ measurements4,5,6). The terms bitumen as used in those papers refers to hydrocarbons with viscosity values 1,000 cp -10,000 cp and higher at ambient temperature conditions °(20OC- 30OC). The real NMR application for bitumen/water content determination in porous media can be affected by uncertainties related to hardware problems and problems related to application of the misinterpretive algorithms and software. The experimental results demonstrate that NMR response from bitumen in porous media is similar to signals from capillary bound water5,6. The polar components from bitumen, asphaltenes first of all, can influence process of wettability alteration. The wettability alteration leads to un-proper usage of the popular interpretive models7 and caused misinterpretation from conventional logs and NMR Logs6. The laboratory NMR experiments performed at different temperatures5 demonstrate that ∼95% of total amplitude of NMR signal recorded from bitumen is related to spectral bins presented in frame of T2<1ms. Therefore impossibility to deliver whole NMR signal representing bitumen in porous media is caused by hardware limitation (long Time-to-Echo [TE] in MRIL tool) or can be affected by in-situ conditions. Investigation of bitumen content in totally invaded near-wellbore zone is usual for CMR tool application. This kind of situation leads some authors to conclusion about difficulties to quantify heavy hydrocarbons' content in porous media from NMR measurement alone6.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".