312 km Mubadala Ruby Field Subsea Gas Pipeline Dent Investigation and Assessment

2021 ◽  
Author(s):  
F. Purnawarman
Keyword(s):  
Best Practices ◽  
Engineering Calculation ◽  
Gas Pipeline ◽  
Assessment Process ◽  
Long Distance ◽  
Side Scan Sonar ◽  
Standard Code ◽  
Engineering Best Practices ◽  
Complex Methods ◽  
Investigation Cost

Mubadala Petroleum (pipeline operator) responded to a pig stuck in a 312 km subsea gas pipeline. A bi-di pig was found to be stuck and after being rescued, the bi-di pig discs were found to be dented. An investigation and assessment are conducted to found what causes pig stuck, to solve the issue of a pig stuck, and to execute all the processes during pipeline operation as effectively and safest way as possible. The investigation started by defining what makes dented bi-di pigs, is an object inside the pipeline or pipeline deformation. A series of conformity techniques are applied by calculation to predict exact pig stuck location, using the backpressure method. A series of inspections were conducted from simple to complex way. From a smart pig that resulted from pig damage to side-scan sonar (SSS) inspection, and visual inspection at predicted location due to cost and time limit. The result is the investigation and assessment process to find dent location within 312 km seabed are executed in a time effective and cost reduced way, under 2 years for all activity for technical process and field execution duration. The sequence also escalating from lowest cost and easiest methods (engineering calculation) to the highest and more complex methods and cost (inspection and survey). The accurate result of prediction confirmed by the dent location and damages at KP 111.89 and 30 meters water depth. The investigation methodology also complies with the requirement of regulation, company specs, standard/code, and engineering best practices. The benefits of this paper are as a reference to conduct series of pipeline damage investigations for a long-distance and remote subsea pipeline. The investigation sequence can apply to many cases with accurate prediction to reduce investigation cost, time, complexity, and risk.

High Consequence Areas (HCAs) Assessment for Long-Distance Oil/Gas Pipeline

ICPTT 2011 ◽  
2011 ◽  
Author(s):  
Jianlin Ma ◽  
Liqiong Chen ◽  
PengpPh.D. Zhang ◽  
Sizhong Wang
Keyword(s):  
Gas Pipeline ◽  
Long Distance ◽  
Oil Gas

Flow Assurance in Deep-Water Gas Pipelines: Locating Hydrate Plugging Position in a Fast Way

2021 ◽  
Author(s):  
Jing Yu ◽  
Cheng Hui ◽  
Chao Wen Sun ◽  
Zhan Ling Zou ◽  
Bin Lu Zhuo ◽  
...  
Keyword(s):  
Pressure Drop ◽  
Deep Water ◽  
Pipe Wall ◽  
Gas Pipeline ◽  
Flow Assurance ◽  
Gas Pipelines ◽  
Long Distance ◽  
Hydrate Layer ◽  
Hydrate Deposition ◽  
Water Gas

Abstract Hydrate-associated issues are of great significance to the oil and gas sector when advancing the development of offshore reservoir. Gas hydrate is easy to form under the condition featuring depressed temperature and elevated pressure within deep-water gas pipeline. Once hydrate deposition is formed within the pipelines, the energy transmission efficiency will be greatly reduced. An accurate prediction of hydrate-obstruction-development behavior will assist flow-assurance engineers to cultivate resource-conserving and environment-friendly strategies for managing hydrate. Based on the long-distance transportation characteristics of deep-water gas pipeline, a quantitative prediction method is expected to explain the hydrate-obstruction-formation behavior in deep-water gas pipeline throughout the production of deep-water gas well. Through a deep analysis of the features of hydrate shaping and precipitation at various locations inside the system, the advised method can quantitatively foresee the dangerous position and intensity of hydrate obstruction. The time from the start of production to the dramatic change of pressure drop brought about by the deposition of hydrate attached to the pipe wall is defined as the Hydrate Plugging Alarm Window (HPAW), which provides guidance for the subsequent hydrate treatment. Case study of deep-water gas pipeline constructed in the South China Sea is performed with the advised method. The simulation outcomes show that hydrates shape and deposit along pipe wall, constructing an endlessly and inconsistently developing hydrate layer, which restricts the pipe, raises the pressure drop, and ultimately leads to obstruction. At the area of 700m-3200m away from the pipeline inlet, the hydrate layer develops all the more swiftly, which points to the region of high risk of obstruction. As the gas-flow rate increases, the period needed for the system to shape hydrate obstruction becomes less. The narrower the internal diameter of the pipeline is, the more severe risk of hydrate obstruction will occur. The HPAW is 100 days under the case conditions. As the concentration of hydrate inhibitor rises, the region inside the system that tallies with the hydrate phase equilibrium conditions progressively reduces and the hydrate deposition rate slows down. The advised method will support operators to define the location of hydrate inhibitor injection within a shorter period in comparison to the conventional method. This work will deliver key instructions for locating the hydrate plugging position in a fast way in addition to solving the problem of hydrate flow assurance in deep-water gas pipelines at a reduced cost.

Programmatic modeling for biological systems

2021 ◽  
Author(s):  
Alexander L.R. Lubbock ◽  
Carlos F. Lopez
Keyword(s):  
Software Engineering ◽  
Best Practices ◽  
Computational Modeling ◽  
User Interfaces ◽  
Model Development ◽  
List Type ◽  
Cellular Processes ◽  
Domain Specific ◽  
Community Benefits ◽  
Engineering Best Practices

AbstractComputational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.HighlightsProgrammatic modeling combines computational modeling with software engineering best practices.An executable model enables users to leverage all available resources from the language.Community benefits include improved collaboration, reusability, and reproducibility.Python has multiple modeling frameworks with a broad, active scientific ecosystem.

scholarly journals The Critical Role of Instructional Response for Identifying Dyslexia and Other Learning Disabilities

2020 ◽  
Vol 53 (5) ◽  
pp. 343-353 ◽  
Author(s):  
Jeremy Miciak ◽  
Jack M. Fletcher
Keyword(s):  
Best Practices ◽  
Empirical Evidence ◽  
Critical Role ◽  
Fidelity Of Implementation ◽  
Assessment Process ◽  
Evidence Based ◽  
Inadequate Response ◽  
Identification Process ◽  
Based Instruction

This article addresses the nature of dyslexia and best practices for identification and treatment within the context of multitier systems of support (MTSS). We initially review proposed definitions of dyslexia to identify key commonalities and differences in proposed attributes. We then review empirical evidence for proposed definitional attributes, focusing on key sources of controversy, including the role of IQ, instructional response, as well as issues of etiology and immutability. We argue that current empirical evidence supports a dyslexia classification marked by specific deficits in reading and spelling words combined with inadequate response to evidence-based instruction. We then propose a “hybrid” dyslexia identification process built to gather data relevant to these markers of dyslexia. We argue that this assessment process is best implemented within school-wide MTSS because it leverages data routinely collected in well-implemented MTSS, including documentation of student progress and fidelity of implementation. In contrast with other proposed methods for learning disability (LD) identification, the proposed “hybrid” method demonstrates strong evidence for valid decision-making and directly informs intervention.

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