Determination of Oil and Water Compositions of Oil/Water Emulsions Using Low Field NMR Relaxometry
Bibliographic record
Abstract
Abstract During production operations in heavy oil and bitumen formations where thermal recovery methods are applied, the fluids produced are often in the form of emulsions. This is also true in non-thermal recovery methods whenever oil and water are coproduced, but to a lower degree of severity. Conventional flow measuring devices are capable of measuring oil and water streams when they are segregated, but they fail when oil-in-water or water-in-oil emulsions form. Conventional methods are also not reliable when there are solids flowing in the stream. Low field NMR relaxometry was successfully tested as a tool for accurately measuring the oil and water content of such streams with and without emulsions present in the samples. The method was proved to be at least as good as conventional extraction methods (i.e., Dean-Stark). The technology was tested with both artificially and naturally occurring emulsified streams with accuracy better than 96﹪. This extremely encouraging result led to the design of an online NMR relaxometer for oil/water stream measurements under the conditions encountered in the production of heavy oil and bitumen. Introduction In the recovery of bitumen, viscosity reduction becomes important, both below and above the ground. The addition of a liquid diluent is thought to break down or weaken the intermolecular forces which create high viscosity in bitumen(1). The effect is so dramatic that the addition of even 5﹪ diluent can cause a viscosity reduction in excess of 80%; thus, facilitating the in situ recovery and pipe line transportation of bitumen. The knowledge of the bitumen-diluent viscosity is highly important, since without it, calculations in upgrading process, in situ recovery, well simulation, heat transfer, fluid flow, and a variety of other engineering problems would be difficult or impossible to solve. This paper presents the development of a simple correlation to predict the viscosity of binary mixtures of bitumen-diluent in any proportion. Experimental The data used for the development of the correlation was TABLE 1: Bitumen data at 30 °CDATA[C. Available In Full Paper. TABLE 2: Diluent data at 30 °CDATA[C. Available In Full Paper. obtained from Wallace et al.(2) and Wallace and Henry(3).The data consisted of a total of 99 points obtained from three bitumens and five diluents, respectively, listed in Tables 1 and 2. Each of these bitumen samples was diluted at 30 °CDATA[C to 5, 10, 25, 50 and 75 weight ﹪ diluent with each of the diluents. After mixing, the samples were reweighed, and any weight loss was attributed to solvent evaporation. The diluent weight fractions were adjusted accordingly, and the viscosities of the mixtures measured. For a detailed account of experimental procedures, refer to Wallace and Henry(3). Correlation Development Many correlations have been developed to predict the viscosity characteristics of bitumen-diluent mixtures(1-6). While several have been successful in making these predictions, most are cumbersome to use. Low Field Nuclear Magnetic Resonance (NMR) relaxometry techniques were developed in the laboratory to enhance and support comparable NMR logging tools that are currently used downhole.
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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 | 0.001 | 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".