Steel Lazy Wave Riser Optimization Using Artificial Intelligence Tool

2020 ◽  
Author(s):  
Mayank Lal ◽  
Abhilash Sebastian ◽  
Feng Wang ◽  
Xiaohua Lu
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Design Process ◽  
Oil And Gas ◽  
Time Domain Analysis ◽  
Fatigue Performance ◽  
Wave Form ◽  
Steel Catenary Risers ◽  
Performance Drivers ◽  
Floating Platforms

Abstract Use of steel lazy wave risers has increased as oil and gas developments are happening in deeper waters or in parts of the world with no pipeline infrastructure. These developments utilize FPSO’s with offloading capabilities necessary for these developments. However, due to more severe motions compared to other floating platforms, traditional catenary form of risers are unsuitable for such developments and this is the reason Steel lazy wave risers (SLWR) are required. SLWRs have shown to have better strength and fatigue performance and lower tensions at the hang-off compared to steel catenary risers. A suitable Lazy-Wave form of the catenary riser is achieved by targeted placement of a custom configured buoyancy section. The strength and fatigue performance of steel lazy wave risers are governed by parameters such as length to start of this buoyancy section, length of the buoyancy section, hang-off angle and the buoyancy factor. Achieving these key performance drivers for a SLWR takes several iterations throughout the design process. In this paper, genetic algorithm which is an artificial intelligence optimization tool has been used to automate the generation of an optimized configuration of a steel lazy wave riser. This will enable the riser designer to speed up the riser design process to achieve the best location, coverage and size of the buoyancy section. The results that the genetic algorithm routine produces is compared to a parametric study of steel lazy wave risers varying the key performance drivers. The parametric analysis uses a regular wave time domain analysis and captures trends of change in strength and fatigue performance with change in steel lazy wave parameters.

Development and Qualification of Alternative Solutions for Improved Fatigue Performance of Deepwater Steel Catenary Risers

2007 ◽  
Author(s):  
Rajiv K. Aggarwal ◽  
Marcio M. Mourelle ◽  
Steinar Kristoffersen ◽  
Henri Godinot ◽  
Pedro Vargas ◽  
...  
Keyword(s):  
Laboratory Testing ◽  
Fatigue Testing ◽  
High Strength ◽  
Operating Conditions ◽  
Fatigue Performance ◽  
Steel Catenary Riser ◽  
Regulatory Bodies ◽  
Steel Catenary Risers ◽  
Floating Platforms ◽  
Floating Systems

Several initiatives have been undertaken by the operators, engineering companies, product manufacturers, and regulatory bodies to enable increased use of steel catenary riser (SCR) design in development of deepwater and ultra-deepwater fields. Some of these efforts focus on improvement in understanding of soil-structure interaction at SCR touch down zone (TDZ) and its impact on fatigue damage estimates through analytical studies, laboratory testing, or in-field monitoring of SCR behavior. Through recent studies and laboratory testing work for floating platforms with SCR, the need for significant enhancement of SCR design at TDZ through implementation of alternate solutions has been identified. This paper presents a summary of the work undertaken in a Joint Industry Project (JIP) during 2004 to 2007 [1, 2] to develop solutions and undertake qualification tasks for four alternatives with potential to improve fatigue performance at TDZ by factor of up to 10 or more. The solutions considered at SCR TDZ include: thick light-weight coating over steel riser sections; steel riser sections with upset ends; high strength steel riser sections with integral connectors; and a titanium segment. The major qualification tasks undertaken for each solution will be identified and discussed. The qualification program undertaken for each solution varied and in some cases, it also included manufacturing of samples, laboratory and full-scale fatigue testing, and post-failure evaluation. Through significant qualification activities undertaken in this JIP, progress has been made to bring these solutions to project ready state for their consideration at the frond end engineering design (FEED) stage. Such design enhancements would enable increase in selection of SCR design for production and export riser applications under severe operating conditions, harsh environment, and floating systems with high motions.

Time Domain Analysis of a Multi-Component Mooring and Steel Catenary Risers System in Ultra Deepwater

2011 ◽  
Author(s):  
Umaru Muhammad Ba ◽  
Hoi-Sang Chan
Keyword(s):  
Time Domain ◽  
Oil And Gas ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Mooring System ◽  
Oil Exploration ◽  
New Methods ◽  
Oil And Gas Resources ◽  
Steel Catenary Risers ◽  
Wave Induced

With the gradual depletion of oil and gas resources onshore as well as shallow offshore waters, oil exploration is gradually moving deeper and deeper into the seas. One of the major means of oil exploration at such locations is by way of a Floating Production Storage and Offloading (FPSO) system. Because of the ever increasing depths of exploration and the prevailing harsh environmental conditions, there is a need to constantly re-evaluate or develop new methods for mooring system analyses. This paper presents the analysis of a coupled multi-component mooring/riser/FPSO system in ultra deepwater due to the first and second order wave induced motions in time-domain. Analysis was carried out for two case studies and the results were found to be quite practical.

scholarly journals Optimization of Air Gap Length and Capacitive Auxiliary Winding in Three-Phase Induction Motors Based on a Genetic Algorithm

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4407
Author(s):  
Mbika Muteba
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Power Factor ◽  
High Speed ◽  
High Efficiency ◽  
Air Gap ◽  
Element Analysis ◽  
Three Phase ◽  
High Torque ◽  
Cage Induction Motor

There is a necessity to design a three-phase squirrel cage induction motor (SCIM) for high-speed applications with a larger air gap length in order to limit the distortion of air gap flux density, the thermal expansion of stator and rotor teeth, centrifugal forces, and the magnetic pull. To that effect, a larger air gap length lowers the power factor, efficiency, and torque density of a three-phase SCIM. This should inform motor design engineers to take special care during the design process of a three-phase SCIM by selecting an air gap length that will provide optimal performance. This paper presents an approach that would assist with the selection of an optimal air gap length (OAL) and optimal capacitive auxiliary stator winding (OCASW) configuration for a high torque per ampere (TPA) three-phase SCIM. A genetic algorithm (GA) assisted by finite element analysis (FEA) is used in the design process to determine the OAL and OCASW required to obtain a high torque per ampere without compromising the merit of achieving an excellent power factor and high efficiency for a three-phase SCIM. The performance of the optimized three-phase SCIM is compared to unoptimized machines. The results obtained from FEA are validated through experimental measurements. Owing to the penalty functions related to the value of objective and constraint functions introduced in the genetic algorithm model, both the FEA and experimental results provide evidence that an enhanced torque per ampere three-phase SCIM can be realized for a large OAL and OCASW with high efficiency and an excellent power factor in different working conditions.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

2021 ◽  
Vol 73 (01) ◽  
pp. 12-13
Author(s):  
Manas Pathak ◽  
Tonya Cosby ◽  
Robert K. Perrons
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Autonomous Systems ◽  
Oil And Gas Industry ◽  
Human Judgment ◽  
Gas Industry ◽  
The Media ◽  
And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Human Robot Interaction ◽  
Gas Industry ◽  
Deep Dive ◽  
The Future ◽  
And Robotics

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?

Numerical Simulation on Mooring Performance of LNG-FPSO System in Realistic Seas

2005 ◽  
Author(s):  
Yoshiyuki Inoue ◽  
Md. Kamruzzaman
Keyword(s):  
Time Domain ◽  
Oil And Gas ◽  
Three Dimensional ◽  
Time Domain Analysis ◽  
Analysis Tool ◽  
Motion Response ◽  
Non Linear ◽  
Floating System ◽  
Exciting Forces ◽  
The Time Domain

The LNG-FPSO concept is receiving much attention in recent years, due to its active usage to exploit oil and gas resources. The FPSO offloads LNG to an LNG carrier that is located close to the FPSO, and during this transfer process two large vessels are in close proximity to each other for daylong periods of time. Due to the presence of neighboring vessel, the motion response of both the vessels will be affected significantly. Hydrodynamic interactions related to wave effects may result in unfavorable responses or the risk of collisions in a multi-body floating system. Not only the motion behavior but also the second order drift forces are influenced by the neighboring structures due to interactions of the waves among the structures. A study is made on the time domain analysis to assess the behavior and the operational capability of the FPSO system moored in the sea having an LNG carrier alongside under environmental conditions such as waves, wind and currents. This paper presents an analysis tool to predict the dynamic motion response and non-linear connecting and mooring forces on a parallel-connected LNG-FPSO system due to non-linear exciting forces of wave, wind and current. Simulation for the mooring performance is also investigated. The three-dimensional source-sink technique has been applied to obtain the radiation forces and the transfer function of wave exciting forces on floating multi-bodies. The hydrodynamic interaction effect between the FPSO and the LNG carrier is included to calculate the hydrodynamic forces. For the simulation of a random sea and also for the generation of time depended wind velocity, a fully probabilistic simulation technique has been applied. Wind and current loads are estimated according to OCIMF. The effects of variations in wave, wind and current loads and direction on the slowly varying oscillations of the LNG and FPSO are also investigated in this paper. Finally, some conclusions are drawn based on the numerical results obtained from the present time domain simulations.

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