BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Tendenci - The Open Source AMS for Associations//Tendenci Codeba se MIMEDIR//EN BEGIN:VEVENT DESCRIPTION:--- This iCal file does *NOT* confirm registration.\r\nEvent d etails subject to change. ---\r\nhttps://www.spegcs.org/events/2718/\r\n\r \nEvent Title: SPEI: Advanced Data-Driven Analytics for Reservoir and Prod uction Management of Shale Assets Training Course\r\nStart Date / Time: No v 18, 2014 07:30 AM US/Central\r\nLocation: Moody Gardens Hotel and Confer ence Center\r\nSpeaker: Shahab D. Mohaghegh, President and CEO of Intellig ent Solutions, Inc. (ISI)\r\nGoogle\r\nhttp://maps.google.com/maps?q=Seven +Hope+Boulevard,Galveston,Texas,77554\r\n\r\nForecast\nhttp://www.weather. com/weather/monthly/77554\r\n\r\n \r\nThe main idea behind data-driven sol utions is that &ldquo\;Data&rdquo\; can provide the foundation of new solu tions. Using &ldquo\;Data&rdquo\; as the main building block of models is the new paradigm in science and technology. Data-driven analytics and pred ictive modeling incorporates pattern recognition capabilities of artificia l intelligence and data mining, through machine learning, to solve complex and non-linear engineering problems.\r\nIn the context of oil and gas pro duction from shale assets, &lsquo\;Hard Data&rsquo\; refers to field measu rements. This is the data that can be readily and usually is, measured dur ing the operation. In most shale assets &lsquo\;Hard Data&rsquo\; associat ed with hydraulic fracturing is measured and recorded in reasonable detail and are usually available.\r\nAttendees will become familiar with the fun damentals of data-driven analytics and the most popular techniques that ar e used to perform such tasks such as conventional statistics, artificial n eural networks and fuzzy set theory.\r\nThis course will demonstrate throu gh actual case studies (real field data from hundreds of shale wells) how to build data-driven predictive model and how to use them in order to perf orm analysis.\r\nTopics Include:\r\n\r\nHow to treat data in the context o f data-driven analytics\r\nOrganize end prepare the data for predictive mo deling\r\nHow to make sure that the physics of fluid flow in shale in hono red during the predictive analytics\r\nHow to build predictive models usin g data as the main building block\r\nHow to avoid over-training (memorizat ion) while promote generalization\r\n\r\nLearning Level\r\nIntermediate\r\ nCourse Length\r\n1 or 2 Days\r\nWhy Attend?\r\nApplication of data-driven analytics and predictive modeling in the oil and gas industry is fairly n ew. A handful of researchers and practitioners have concentrated their eff orts on providing the next generation of tools that incorporates this tech nology, for the petroleum industry.\r\nThese advance techniques are an int egrated part of many new technologies used by everyone on their day-to-day lives such as smart automatic-transmission in many cars, detecting explos ives in the airport security systems, providing smooth rides in subway sys tems and preventing fraud in use of credit cards. They are extensively use d in the financial market to predict chaotic stock market behavior, or opt imize financial portfolios.\r\nWho Should Attend\r\nThis course is intende d for completion engineers, production engineers and managers, reservoir e ngineers, geoscientists, asset managers, and team leaders.\r\nCEUs\r\n0.8 or 1.6 CEUs (Continuing Education Units) will be awarded for this 1-day co urse.\r\n \r\nRegistration and Information--- This iCal file does *NOT* co nfirm registration.Event details subject to change. ---\r\n\r\n--- By Tend enci - The Open Source AMS for Associations ---\r\n UID:uid2718@spegcs.org SUMMARY:SPEI: Advanced Data-Driven Analytics for Reservoir and Production Management of Shale Assets Training Course DTSTART:20141118T133000Z DTEND:20141118T230000Z CLASS:PUBLIC PRIORITY:5 DTSTAMP:20240328T113921Z TRANSP:OPAQUE SEQUENCE:0 LOCATION:Moody Gardens Hotel and Conference Center X-ALT-DESC;FMTTYPE=text/html:
 \ ;
The main idea behind data-driven solutions is that &ldquo\;Data&r dquo\; can provide the foundation of new solutions. Using &ldquo\;Data&rdq uo\; as the main building block of models is the new paradigm in science a nd technology. Data-driven analytics and predictive modeling incorporates pattern recognition capabilities of artificial intelligence and data minin g, through machine learning, to solve complex and non-linear engineering p roblems.
In the context of oil and gas production from shale assets , &lsquo\;Hard Data&rsquo\; refers to field measurements. This is the data that can be readily and usually is, measured during the operation. In mos t shale assets &lsquo\;Hard Data&rsquo\; associated with hydraulic fractur ing is measured and recorded in reasonable detail and are usually availabl e.
Attendees will become familiar with the fundamentals of data-dri ven analytics and the most popular techniques that are used to perform suc h tasks such as conventional statistics, artificial neural networks and fu zzy set theory.
This course will demonstrate through actual case st udies (real field data from hundreds of shale wells) how to build data-dri ven predictive model and how to use them in order to perform analysis.
Topics Include:
Intermediate
1 or 2 Days
Application of data-driven analyt ics and predictive modeling in the oil and gas industry is fairly new. A h andful of researchers and practitioners have concentrated their efforts on providing the next generation of tools that incorporates this technology, for the petroleum industry.
These advance techniques are an integr ated part of many new technologies used by everyone on their day-to-day li ves such as smart automatic-transmission in many cars, detecting explosive s in the airport security systems, providing smooth rides in subway system s and preventing fraud in use of credit cards. They are extensively used i n the financial market to predict chaotic stock market behavior, or optimi ze financial portfolios.
This course is intended for completion engineers, production engineers and managers, rese rvoir engineers, geoscientists, asset managers, and team leaders.
0.8 or 1.6 CEUs (Continuing Education Units) will be awarded for this 1-day course.