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Title: rtmApp: A SaaS Application for Reactive Transport Modeling

Abstract

Reactive transport models (RTMs) are quantitative tools to analyze the interaction between physical, chemical, and biological processes in various material and energy systems. Oil and gas, mining, nuclear waste management, and environmental consulting companies use RTMs to acquire critical information which are essential to: estimate the oil and gas production rates under different hydraulic fracturing and enhanced oil recovery strategies, minimize the risk of groundwater contamination by acid mine drainage, assess the safety of cement-based engineered barrier systems in nuclear waste repositories, and design the efficient treatment and remediation strategies for contaminated sites. In order to benefit gamut of industries which face real-world engineering and scientific problems spanning from nano to kilometer length scales, multiscale RTMs are needed. For example, reservoir engineers in oil and gas industry need simulation tools which are capable of combining information of the entire reservoir in kilometer scale with micro-scale properties of various rock samples. With engineers and scientists from various industrial and academic disciplines pursuing online technologies to support their operation and research, web-based applications have been emerged as primary tools. Worldwide access to data anytime and with any mobile device as well as offering flexible, cost-effective, on-demand and pay-as-you-go model have led tomore » increased demand for SaaS (Software as a Service) applications. Furthermore, cloud-based solutions have decreased upfront and maintenance costs of hardware and software for large-scale computational problems. RTMs, still have not been benefited immensely from SaaS advancements. During DoE-SBIR Phase I project, we built a scalable, highly-available, and secure cloud architecture on Amazon Web Service (AWS) in order to host rtmApp (PoreStudio) web application. PoreStudio is the first cloud-based multiscale reactive flow simulator. In this project, first we developed a novel mathematical model which is capable of simulating the interaction between physical, chemical, and biological processes in natural and man-made porous and fractured media (e.g. reservoir rocks). Then, we migrated the developed model to AWS cloud environment which was designed based on best practices considering factors such as scalability, availability, automation and security. Finally, a pilot project was successfully conducted to test the behavior of designed cloud architecture under simultaneous computational loading. Leading industrial and academic R&D groups participated in the pilot project.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
AquaNRG Consulting Inc
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1491356
Report Number(s):
DOE-AquaNRG-0018510
DOE Contract Number:  
SC0018510
Type / Phase:
SBIR (Phase I)
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; 42 ENGINEERING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 15 GEOTHERMAL ENERGY; 02 PETROLEUM; Reactive Transport Model, Computational Fluid Dynamics, Cloud Computing, Lattice Boltzmann, Geochemistry

Citation Formats

Shafei, Babak, Basagaoglu, Hakan, Chaudhary, Kuldeep, DiNenno, Flak, and Leks, Sebastian. rtmApp: A SaaS Application for Reactive Transport Modeling. United States: N. p., 2019. Web.
Shafei, Babak, Basagaoglu, Hakan, Chaudhary, Kuldeep, DiNenno, Flak, & Leks, Sebastian. rtmApp: A SaaS Application for Reactive Transport Modeling. United States.
Shafei, Babak, Basagaoglu, Hakan, Chaudhary, Kuldeep, DiNenno, Flak, and Leks, Sebastian. Mon . "rtmApp: A SaaS Application for Reactive Transport Modeling". United States.
@article{osti_1491356,
title = {rtmApp: A SaaS Application for Reactive Transport Modeling},
author = {Shafei, Babak and Basagaoglu, Hakan and Chaudhary, Kuldeep and DiNenno, Flak and Leks, Sebastian},
abstractNote = {Reactive transport models (RTMs) are quantitative tools to analyze the interaction between physical, chemical, and biological processes in various material and energy systems. Oil and gas, mining, nuclear waste management, and environmental consulting companies use RTMs to acquire critical information which are essential to: estimate the oil and gas production rates under different hydraulic fracturing and enhanced oil recovery strategies, minimize the risk of groundwater contamination by acid mine drainage, assess the safety of cement-based engineered barrier systems in nuclear waste repositories, and design the efficient treatment and remediation strategies for contaminated sites. In order to benefit gamut of industries which face real-world engineering and scientific problems spanning from nano to kilometer length scales, multiscale RTMs are needed. For example, reservoir engineers in oil and gas industry need simulation tools which are capable of combining information of the entire reservoir in kilometer scale with micro-scale properties of various rock samples. With engineers and scientists from various industrial and academic disciplines pursuing online technologies to support their operation and research, web-based applications have been emerged as primary tools. Worldwide access to data anytime and with any mobile device as well as offering flexible, cost-effective, on-demand and pay-as-you-go model have led to increased demand for SaaS (Software as a Service) applications. Furthermore, cloud-based solutions have decreased upfront and maintenance costs of hardware and software for large-scale computational problems. RTMs, still have not been benefited immensely from SaaS advancements. During DoE-SBIR Phase I project, we built a scalable, highly-available, and secure cloud architecture on Amazon Web Service (AWS) in order to host rtmApp (PoreStudio) web application. PoreStudio is the first cloud-based multiscale reactive flow simulator. In this project, first we developed a novel mathematical model which is capable of simulating the interaction between physical, chemical, and biological processes in natural and man-made porous and fractured media (e.g. reservoir rocks). Then, we migrated the developed model to AWS cloud environment which was designed based on best practices considering factors such as scalability, availability, automation and security. Finally, a pilot project was successfully conducted to test the behavior of designed cloud architecture under simultaneous computational loading. Leading industrial and academic R&D groups participated in the pilot project.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Technical Report:
This technical report may be released as soon as September 30, 2023
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