Pipelines Slugging and Mitigation: Case Study for Stability and ProductionOptimization
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pipelines Slugging and Mitigation: Case Study for Stability and Production Optimization Yula Tang; Yula Tang Chevron Corp. Search for other works by this author on: This Site Google Scholar Thomas John Danielson Thomas John Danielson ConocoPhillips Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. Paper Number: SPE-102352-MS https://doi.org/10.2118/102352-MS Published: September 24 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Tang, Yula, and Thomas John Danielson. "Pipelines Slugging and Mitigation: Case Study for Stability and Production Optimization." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. doi: https://doi.org/10.2118/102352-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu 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 AbstractThe ConocoPhillips Alpine facility, on the Alaskan North Slope, has experienced slugging problems severe enough to trip the high-high inlet separator level, causing frequent plant shutdowns, and loss of production of 110 kbbl/d. A slugging study was commissioned to investigate the cause of the existing CD-2 pipeline slugging, and possible mitigation procedures, which could alleviate and/or eliminate slugging. Further, the Alpine expansion called for an additional two pipelines (CD-3 and CD-4) to be brought into Alpine inlet separator.Slugging mechanism and instability analysis were performed. The instability is due to combination of its low flow rate, overly-sized pipeline ID and unfavorable pipeline profile. Flow pattern transition exists at the low spots and liquid accumulates and blocks the flow. In the low pressure system, once gas blows out and system pressure drops, the pipeline inlet gas increases velocity and picks up a new hydrodynamic slug. This slug moves through the road crossing and the pipe rack riser, becoming a long slug which arrives at the separator.In this study, a slug-tracking model with separator gas/liquid PID controllers was built to reproduce the field SCADA data. A remarkably good match of pressure variations, slugging frequency and liquid level was achieved. A sensitivity study was performed to investigate the effective and practical ways to suppress slugging in the existing CD-1 and CD-2 pipeline. Finally, a combined control was recommended by installing a by-pass control valve at the butterfly valve location. The by-pass inlet control valve before the separator acquires separator liquid level signal, and actuates when the separator liquid level exceeds the set value. This significantly reduces slugging effect on separator performance.The slugging model and results based on the existing CD-2 pipeline were applied to the future expanded CD-3 and CD-4 pipeline study. Some conclusions were drawn from the slugging behavior.IntroductionIn many oil and gas developments with multiphase flowlines, production instability due to slugging is a major flow assurance concern. Slugging initiates oscillations, puts excessive demands upon the separation and operation, and increases the wear and tear of equipment. Large liquid surges can cause poor performance, separator shut down, high pressure trips, or flaring.Slugging can be characterized by periodical change of pressure and gas/liquid flow. The slugging severity depends on slugging types. There are three types of slugging:Hydrodynamic slugs: a feature of the slug flow regime where slugs are continuously formed due to instability of waves at certain gas-liquid flow rates. Generally, hydrodynamic slugs do not exceed 20 times of pipe diameters if there is no obvious inclination change.Operationally induced surges: generated by changing the flow conditions from one steady state to another, such as restart, flow rate ramp-up or pigging operations. The generated liquid surge can upset the system.Terrain induced slugs: also called severe slugs, caused by accumulation and periodic purging of liquid in flowline dips at low flow rates; Keywords: production monitoring, production control, Reservoir Surveillance, Upstream Oil & Gas, controller, production logging, frequency, liquid level, pipeline transient behavior, slug catcher Subjects: Well & Reservoir Surveillance and Monitoring, Pipelines, Flowlines and Risers, Production logging, Piping design and simulation, Pipeline transient behavior This content is only available via PDF. 2006. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it