Classifying Crude Oil Emulsions Using Chemical Demulsifiers and Statistical Analyses
Bibliographic record
Abstract
Classifying Crude Oil Emulsions Using Chemical Demulsifiers and Statistical Analyses Michael K. Poindexter; Michael K. Poindexter Ondeo Nalco Energy Services Search for other works by this author on: This Site Google Scholar Shaokun Chuai; Shaokun Chuai Ondeo Nalco Energy Services Search for other works by this author on: This Site Google Scholar Robert A. Marble; Robert A. Marble Ondeo Nalco Energy Services Search for other works by this author on: This Site Google Scholar Samuel C. Marsh Samuel C. Marsh Ondeo Nalco Energy Services Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84610-MS https://doi.org/10.2118/84610-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Poindexter, Michael K., Chuai, Shaokun, Marble, Robert A., and Samuel C. Marsh. "Classifying Crude Oil Emulsions Using Chemical Demulsifiers and Statistical Analyses." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84610-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractCrude oil emulsions are highly complex mixtures that can be stabilized by a number of naturally occurring species and conditions (e.g. asphaltenes, resins, acids, solids, solvency, viscosity, temperature, etc.). Emulsion resolution is often accomplished using chemicals. Optimization of chemical treatment is generally accomplished in the field by bottle testing a large number of potential chemical intermediates and their combinations. Successful chemical formulations are able to drop water rapidly, provide relatively clean interfaces, and produce dry, saleable oil.The very nature of bottle testing produces a large amount of data. Some of the data provides information regarding water drop, while part focuses on breaking the emulsion (i.e. producing dry oil and clean interfaces). To summarize results from multiple test sites, a highly structured bottle test was devised to make comparisons among different oilfield emulsions. Thirty-eight chemical intermediates, tested at two dosages, were evaluated at nine different sites. From the testing, ten bottle test performance parameters (four describing water drop, three describing oil dryness, and three describing the oil-water interface) were analyzed using several statistical methods: analysis of variance, multivariate correlations, cluster analysis, and principal component analysis. The analyses revealed a number of interesting trends. For example, the water drop and oil dryness parameters were found to be more independent of one another than the water drop and interface parameters. These results suggest that water drop and oil dryness are likely governed by two different mechanisms. This work has resulted in several emulsion "maps" where crudes can now be classified with regard to their ability to drop water and break emulsion.IntroductionCrude oils are extremely complex fluids. Numerous separation methods have been used to classify crudes.1 Ongoing research efforts introducing new techniques and more efficient methods of separation continue at a steady pace.2–6 Part of the intent to classify is to better predict crude oil behavior during production, blending, and processing. By building robust predictive models, crude oil production problems can be anticipated and minimized.7As difficult as classifying crudes can be, an additional degree of complexity occurs when water is part of the equation such that water-in-crude oil emulsions result. Most crude oil production has or will have associated water. Many emulsions have a time dependent nature meaning that emulsion stabilization can either increase or decrease over time once the sample emulsion is isolated for initial study. Thus, conclusions drawn from a study at one point in time might not fully agree with other time frames. This aging effect on samples studied in the lab versus the field has been noted.8,9 Work in our laboratory agrees. Many of the field studies discussed in this paper were repeated on the same emulsion but at a later time. In all but one case, the aged emulsion was more difficult to resolve than the field (or fresh) emulsion taken directly from the production facility and tested immediately.This paper will focus only on oilfield emulsions and will introduce a new twist on an established test procedure, namely the bottle test. Various statistical techniques were used to sort through the data with the goal of classifying emulsions at the point where production issues are relevant. These techniques permit different emulsions to be grouped or ranked regarding traits seen in the bottle test.The Bottle TestEmulsions are resolved by a variety of means including separation equipment, heat, time, and specialty chemicals. Optimizing the combination of these factors (especially the latter three factors) can often be accomplished with the bottle test. The bottle test has existed for years10 and continues to be a source of guidance when addressing field emulsion problems. While the exact steps used in the bottle test can vary from tester to tester, the purpose of the test remains the same: among many possibilities identify a treatment protocol that will effectively resolve the emulsion. Emulsion resolution involves producing dry, saleable oil, clean water, and minimal residual emulsion (often referred to as "slop" or "rag"). Keywords: rock/fluid interaction, artificial intelligence, upstream oil & gas, fluid compatibility, demulsifier, interface, water drop, correlation, machine learning, variance Subjects: Production Chemistry, Metallurgy and Biology, Downhole chemical treatments and fluid compatibility This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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".