Viscosity Determination of Heavy Oil and Bitumen Using NMR Relaxometry
Bibliographic record
Abstract
Abstract Knowledge of oil viscosity is important when estimating hydrocarbon reserves and evaluating the potential for waterflooding or EOR processes. This information is especially important in heavy oil and bitumen, as viscosity is usually the major impediment to recovery of these reserves. As oil viscosity increases, obtaining a laboratory measurement is difficult and prone to error, and viscosities measured in the lab may not be representative of field conditions. Nuclear magnetic resonance (NMR) is therefore presented as an attractive alternative method for determining oil viscosity. Several correlations already exist for determining oil viscosity using NMR. Some of these correlations compare the geometric mean T2 relaxation time to oil viscosity, while others relate viscosity to the apparent hydrogen index. This paper examines these different models on a suite of conventional and heavy oil samples. It is concluded that none of the existing models can accurately predict oil viscosity for both conventional and heavy oils, especially for oils with viscosity higher than 20,000 cP. All the measured oil samples show a correlation between oil viscosity and the geometric mean T2 relaxation time, and also between viscosity and relative hydrogen index. This is consistent with what other experimenters have noticed. An empirical model is developed, correlating oil viscosity to both of these parameters. Unlike previous models, this model can accurately predict oil viscosity for both conventional and heavy oil. The wider range of this model makes it useful for laboratory analysis of oil viscosity using NMR. If the results of this model can be applied to in situ oils, NMR can be used as a logging tool to characterize heavy oil and bitumen formations. The model presented in this paper is the first step towards successfully predicting viscosity in situ. Introduction Determination of oil viscosity is extremely important to the development of any potential oil reservoir. If waterflooding is being considered as a recovery scheme, the mobility ratio between oil and water will have a strong effect on the macroscopic sweep efficiency of the waterflood. Likewise, when considering possible EOR schemes, oil viscosity is one of the most serious impediments to the success of these schemes. Oil viscosity is also a required input parameter for reservoir simulation and well testing. In heavy oil and bitumen reservoirs, the high oil viscosity is often the limiting factor to efficient oil recovery. Waterflooding cannot be used in these reservoirs, due to the adverse mobility ratio between oil and water. The oil is so much more viscous than water that injected water will move through the oil in the form of "viscous fingers," which lead to early water breakthrough and poor sweep efficiency. If attempting a miscible solvent or gas flood, knowledge of oil viscosity is necessary for estimating the efficiency of the flood. Due to the negative effects of the adverse mobility ratio, viscosity reduction is the main focus of EOR schemes in heavy oil reservoirs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".