In-situ Viscosity of Heavy Oil: Core and Log Calibrations
Bibliographic record
Abstract
Abstract Having knowledge of oil viscosity variation within reservoirs would be of considerable benefit when producing from heavy oil fields. Previous work has demonstrated that low field NMR bench top instruments can be used to perform measurements of in-situ viscosity. Ideally, if these measurements could be performed on NMR logging tools, viscosity characterization studies could be carried out with using fewer core samples. In this paper, data is presented for a heavy oil reservoir in northern Alberta. A methodology is presented for tuning NMR viscosity estimates to the field in question, and core analysis results are collected, showing that in-situ viscosity predictions are possible in the laboratory. NMR spectra measured in the laboratory are compared to NMR logging tool spectra, in order to determine if results obtained using bench top instruments can be extrapolated to logging tool data. Introduction Canada has significant proven reserves from our oil sands in Saskatchewan and northern Alberta, which constitute some of the largest resource bases in the world. With the decline of conventional oil reserves in Canada, interest is shifting rapidly to the production of this heavy oil. Heavy oil and bitumen are characterized by high fluid viscosity, and density values similar to that of water. The high oil viscosity is the single greatest impediment to the successful recovery of this resource, and the viscosity is directly related to both the technical success of any chosen recovery scheme and the economic value of the oil. As a result, oil viscosity information is key when estimating reserves and developing recovery options from heavy oil and bitumen formations. Viscosity is conventionally measured in two different Methods1. Samples are either taken from the produced fluid from the wellhead, or oil sand samples are taken into the laboratory, and oil is extracted in order to measure its viscosity. The difficulty in making measurements on wellhead samples is that the oil may have been contaminated by diluents or drilling fluid1, or may contain significant emulsified water from thermal operations. This means that viscosity values obtained from wellhead oil samples must be used carefully and should be analyzed in order to ensure that they are truly representative of the oil viscosity in the formation. Measurements on bitumen that has been extracted from core samples are generally more accurate, but also tend to be more expensive. Care must be taken to ensure that enough samples are taken to properly characterize the fluid viscosity in the reservoir. Variations may also be observed between different laboratory results, and in repeat measurements of crude oil samples2. Therefore, if measurements of oil viscosity could be made in-situ, during the initial logging of the reservoir, this could be of considerable benefit to geologists and reservoir engineers seeking to understand their reservoirs. In the past, low field nuclear magnetic resonance (NMR) has been shown to have great potential as a tool for making viscosity measurements. The uses of NMR are many and varied, and the theory has been well explored in the literature3–5.
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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 | 0.000 | 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) | 0.001 | 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 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".