Qualitative and quantitative reservoir bitumen characterization: A core to log correlation methodology
Bibliographic record
Abstract
Abstract Reservoir bitumen is a highly viscous, asphaltene-rich hydrocarbon that can have important effects on reservoir performance. Discriminating between producible oil and reservoir bitumen is critical for recoverable hydrocarbon volume calculations and production planning, yet the lack of resistivity contrast between the two makes it difficult, if not impossible, to make such differentiation using conventional logs. However, the nuclear magnetic resonance (NMR) response in bitumen-rich zones is dominated by short transverse relaxation times (T2) and a low apparent fluid hydrogen index (HIapp), providing an opportunity to identify the presence of reservoir bitumen. Therefore, NMR logging technology becomes crucial in the characterization of reservoirs in which the presence of bitumen may be of concern. We used NMR and other log data to identify and quantify the occurrence of reservoir bitumen in a carbonate reservoir. A thorough petrophysical evaluation was performed using a full suite of logs, formation pressure measurements, and laboratory core analysis data. We discuss several quick methods to identify intervals with a higher chance of reservoir bitumen presence. The short transverse relaxation times (T2) and consequently lower T2 logarithmic mean time values are characteristics of bitumen-rich zones. Another characteristic is low HIapp in these zones and consequently lower NMR porosity estimates when compared to porosity estimates from the density and neutron tools. We analyzed 2D longitudinal-transverse relaxation time (T1-T2) maps for core samples at different depths to confirm the presence of reservoir bitumen in some wells using laboratory low-field NMR data. We observed a high T1/T2 ratio at various depths, which is an indication of high-molecular-weight hydrocarbons. The presence of bitumen at the same depths was confirmed by thin section analysis, and it is the likely cause for failed formation pressure testing attempts at those depth intervals. Partial cleaning of reservoir bitumen-rich core plugs results in helium-injection porosity estimates that are too low, and they are closer to the NMR porosity than to density porosity, the latter being more consistent with actual values. In addition, the grain density (GD) calculated by He injection is significantly lower than the GD estimated from elemental capture spectroscopy and X-ray diffraction techniques. Disregarding these effects complicates the core to log correlation, which is common practice for porosity calculations using the density log. A volumetric rock model was used to reconcile core and log data as well as to calculate the saturation of reservoir bitumen. The methodologies for reservoir bitumen characterization introduced here can be applied successfully in different reservoirs for more reliable and precise reservoir evaluation and production planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".