Advances in Magnetic Resonance Relaxometry for Heavy Oil and Bitumen Characterization
Bibliographic record
Abstract
Abstract This paper offers a summary of the advances in heavy oil and bitumen reservoir characterization and fluid stream monitoring using low field magnetic resonance tools. Both laboratory and field advances are presented. Although the bulk of the work discussed was performed in our laboratory, a selection of other pertinent technologies is also presented. This overview aims at offering the reader a quick reference of what has been achieved in the past ten years and it is hoped that it will be used as a guide for future development in this area. Introduction Low field nuclear magnetic resonance (NMR) relaxometry is a technology that offers significant benefits in reservoir characterization through the magnetic resonance logging tools that are offered by oil and gas service companies(1). These tools can offer measurements of porosity, permeability, mobile and bound fluids and, potentially saturations, if they are properly calibrated. This technology has been active in its latest reincarnation since the middle of the 1980s. It was originally developed with conventional oil and gas reservoirs in Texas and the North Sea. There are currently several excellent reviews and two recommended books for those interested in studying the topic in detail(1–4). Through low field NMR we measure the amount of and the mobility of hydrogen-bearing molecules. For reservoir characterization applications, such molecules translate into gas, water or oil present within a formation. Although the physics of the process are not the focus of this overview, a brief introductory summary is included. For details, the reader is directed to the references above. The measured parameters in NMR are amplitudes of hydrogenbearing signal and relaxation times of hydrogen-bearing molecules. The amplitude is directly proportional to the amount of protons present and it can be correlated to the volume or mass of fluids within the region of measurement. The relaxation time is affected by the relative mobility of the hydrogen-bearing molecules. Thus, as visocsity increases, or the surroundings of the relaxing hydrogen are restricted, then relaxation occurs faster. There are two relaxation times that can be measured: longitudinal (T1) and transverse (T2). The focus of our work deals with transverse relaxation phenomena. Figure 1 is used as the typical figure to explain different types of relaxation spectra obtained when exposing different systems in the standard pulse sequence that is used in logging and laboratory tools alike. The spectrum of bulk water (i.e. water in a beaker) is a simple narrow peak that shows relaxation at T2 of ~2,500 ms. Compared to water, bulk bitumen (viscosity of ~1,000,000 mPas at room temperature) typically relaxes with a broader peak at less than 2 ms. Thus, in principle, a beaker that is half-full of water and half-full of bitumen would show two peaks, as shown in Figure 2. Figure 1,2,3,4 (available in full paper) In this case, the oil is somewhat lighter than that of Figure 1 (viscosity of ~100,000 mPas at room temperature).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".