Implementation and Performance Analysis of a Parallel Oil Reservoir Simulator Tool Using a CG Method on a GPU-Based System

Ismail, Leila; Kassem, Jamal Abou; Qamar, Bibrak

doi:10.1109/uksim.2014.113

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Data sets relevant to the study were sourced from the Kansas Geological Survey (KGS) which a is research and service division of the University of Kansas [12]. The data sets used in the paper were sourced from the KGS because of the wide spread of accessible data for use and the completeness of the data set.…”

Section: Methodsmentioning

confidence: 99%

Developing Parallel Computing Algorithms Using Gpu’s to Determine Oil and Gas Reserves Presented in the Upstream (Exploration) Sector

Boodoo¹,

Joshi²

2020

Proceedings of the International Conference on Emerging Trends in Engineering &Amp; Technology (IConETech-2020)

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Oil and Gas companies keep exploring every new possible method to increase the likelihood of finding a commercial hydrocarbon bearing prospect. Well logging generates gigabytes of data from various probes and sensors. After processing, a prospective reservoir will indicate areas of oil, gas, water and reservoir rock. Incorporating High Performance Computing (HPC) methodologies will allow for thousands of potential wells to be indicative of its hydrocarbon bearing potential. This study will present the use of the Graphics Processing Unit (GPU) computing as another method of analyzing probable reserves. Raw well log data from the Kansas Geological Society (1999-2018) forms the basis of the data analysis. Parallel algorithms are developed and make use of Nvidia’s Compute Unified Device Architecture (CUDA). The results gathered highlight a 5 times speedup using a Nvidia GeForce GT 330M GPU as compared to an Intel Core i7 740QM Central Processing Unit (CPU). The processed results display depth wise areas of shale and rock formations as well as water, oil and/or gas reserves.

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Section: Methodsmentioning

confidence: 99%

Developing Parallel Computing Algorithms Using Gpu’s to Determine Oil and Gas Reserves Presented in the Upstream (Exploration) Sector

Boodoo¹,

Joshi²

2020

Proceedings of the International Conference on Emerging Trends in Engineering &Amp; Technology (IConETech-2020)

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show abstract

“…Thus, an integrated edge-cloud computing system is often used to handle compute-intensive and/or time-critical applications [ 139 , 140 ]. However, the underlying 6G networks should consider the energy efficiency [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ], optimal resource provisioning and scheduling [ 149 , 150 , 151 , 152 ], and contextual-aware application partitioning [ 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 ] requirements of this integrated system.…”

Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

et al. 2018

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Implementation and Performance Analysis of a Parallel Oil Reservoir Simulator Tool Using a CG Method on a GPU-Based System

Cited by 3 publications

References 18 publications

Developing Parallel Computing Algorithms Using Gpu’s to Determine Oil and Gas Reserves Presented in the Upstream (Exploration) Sector

Developing Parallel Computing Algorithms Using Gpu’s to Determine Oil and Gas Reserves Presented in the Upstream (Exploration) Sector

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

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