Improved Methods For Estimating The Viscosity Of Heavy Oils From Magnetic Resonance Data
Bibliographic record
Abstract
Borehole magnetic resonance (MR) is routinely used to estimate the viscosity of light oil, even when it is mixed with water in the pore space of reservoir rock. However light oil methods are inadequate when oil viscosity is above several thousand mPa-s. There have been a number of publications relating magnetic resonance measurements to heavy oil viscosity, but the correlations proposed have not been found to be universally applicable. MR measurements of heavy oil depend not only on the properties of the oil, but also on the details of data acquisition and processing. Thus MR-viscosity correlations must be customized accordingly. Moreover, when water and oil MR signals overlap, new methods are required to prevent the presence of water from corrupting the MR estimation of oil viscosity. Using laboratory measurements on a large number of Canadian and international heavy oil samples, we have developed new correlations, the coefficients of which depend on tool hardware, acquisition modes, and processing algorithms. These are demonstrated to be useful from 10 mPa-s to 1,000,000 mPa-s or more. We also introduce the partitioned hydrogen index method, which uses a porosity model and a new correlation technique to estimate oil viscosity. This is applicable to formations with significant quantities of water. Using MR and rheological measurements over the range 10°C to 100°C, we find the only effects of temperature are through the routine Curie law correction and the effect of temperature on viscosity itself. Magnetic resonanceviscosity correlations do not require explicit temperature dependence. INTRODUCTION Vast quantities of heavy oil exist in many parts of the world. Production economics depends on several factors, one of the most important of which is oil viscosity. Heavy oil reservoirs may contain several grades of oil. For example, a reservoir might be composed of stacked porous beds separated by impermeable layers. Each bed can contain oil with properties different from the oils in adjacent beds. Moreover, viscosity can vary vertically within a single bed. Borehole logging tools are the most accurate and costeffective means of determining properties of fluids found in subsurface geological formations. Borehole magnetic resonance (MR) is routinely used to estimate the viscosity of light oil, even when it is mixed with water in the pore space of reservoir rock. However, commonly employed MR methods are inadequate when applied to heavy oil. The results of MR measurements of heavy oil depend not only on the properties of the oil, but also on tool characteristics and the details of data acquisition and processing. Thus MR-viscosity correlations must be customized for each borehole logging tool and each mode of employment of any given logging tool. Although the relaxation time distribution is a ubiquitous and useful feature of MR log interpretation, its details can be influenced by processing parameters such as the regularization parameter or the lower and upper bounds of the distribution, T2min and T2max. Use of time domain data avoids these issues. A new correlation uses the initial decay rate of an MR echo train to estimate oil viscosity. W SPWLA 49th Annual Logging Symposium, May 25-28, 2008
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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) | 0.000 | 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".