Bibliographic record
Abstract
Now thankful for the continuing recovery from the health and business effects of COVID-19, I sincerely hope that all JPT readers continue to remain safe, healthy, and busy. Nevertheless, flow assurance effects from slugging and various types of solids deposition during engineering, design, maintenance, and operations still create challenging technical issues needing safe, economical solutions. The recurring long-term mitigation of multiple slugging and flow assurance phenomena such as the deposition of wax, erosion, asphaltenes, corrosion, and salt, still demands attention and considerable technical effort. Hydrodynamic slugging (or two-phase liquid/gas flow instability) in pipelines has caused flow capacity loss and intense vibration at the separator. Paper IPTC 22235 describes how this issue can be resolved using foams generated from surfactants introduced into the pipeline to entrap and slow down gas flow. The foam’s behavior was simulated using conventional OLGA (short for “oil and gas”) simulation software. This study showed that foams were able to slow gas velocity sufficiently and reduced liquid fluctuations, thus reducing slugging in a Malaysian pipeline. Accurate foam-viscosity measurements generated from the laboratory are a vital input to the model to understand the behavior of foam and quantify its effect before actual field applications. In the absence of flow loops to simulate slugging in pipelines and to validate the foam’s behavior, the use of the OLGA software still provides the necessary confidence in the application of foams to save time and reduce laborious and complex experimentations. The work described in paper SPE 209475 focuses on detailed investigation of magnesian calcite as the main deposition constituent of solids fouling several onshore and offshore operations of a major operator in the North Sea and Caspian regions. These solids can and do occur as a conventional scale that has perhaps hidden the extent of their prevalence in offshore and onshore operations involving fouling of tubing and restricting production, impeding operation of subsurface safety valves, reducing the efficiency of heaters and coolers offshore, and plugging strainers in the produced-water handling systems offshore. This paper describes how this “pseudoscale” was detected and its prevalence, using field case examples from several operated and nonoperated assets. It describes the theories developed to rationalize magnesian calcite deposition onto production surfaces. The paper then shares dispersant chemical field-trial-performance data and the deployment of electromagnetic fields to determine the effect of changing the zeta potential on the attraction of fine magnesian calcite particles to the surface and, thus, the effect on the fouling rate. Paper OTC 32059 describes an alternative to constant chemical injection or thermodynamic controls such as insulation or heating: the incorporation of a robust omniphobic surface treatment material that has been shown to significantly reduce the adhesion of flow assurance solids, resulting in lower risk for deposition and plugging by gas hydrates, waxes, and asphaltenes. As part of a US Department of Energy study, laboratory-scale tests were performed on a variety of apparatuses. While the results from these tests are promising, some disconnects between the laboratory-scale observations and field-scale testing still exist. The papers highlighted here for suggested additional reading focus on three areas requiring further new analytical tools while providing safe, cost-effective, and reliable operations for flow assurance. These papers present potential novel applications of nanomaterials for flow-restriction detection, slug modeling in flowline bends, and liquid-slugging detection using pressure monitoring. I hope you find them interesting, informative, and technically helpful and appreciate this opportunity to disseminate these technology options to you in this Technology Focus. In addition, I invite you to participate in the Flow Assurance Technical Section to augment more learning on other topics. Finally, I wish the full and complete recovery of your family, work, and SPE activities during these challenging times during the recovery of oil and gas. I have very much enjoyed providing you the latest developments in flow assurance over the past few years and wish you all the best. Recommended additional reading at OnePetro: www.onepetro.org SPE 208233 Slug-Frequency-Prediction Model for Fluid Flow in Flowline Bends by Loveday Igbokwe, Memorial University of Newfoundland, et al. IPTC 22459 Pressure Characteristics of Slug Flow in Horizontal Pipes by Ala AL-Dogail, King Fahd University of Petroleum and Minerals, et al. OTC 31804 Use of Nanomaterials for Early Detection of Flow-Restricting Substances by Stephen George Louis, Albastron Technologies, et al. SPE 209306 Visualization of Liquid Slugging Using Detailed, High-Quality Pressure Monitoring by Ryan David Gordon, Toku Systems, et al.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".