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Bachelor of Technology in Applied Petroleum Engineering Upstream Spl in Specialization in E&P Data Management

Bachelor of Technology in Applied Petroleum Engineering Upstream Spl in Specialization in E&P Data Management

at University of Petroleum and Energy Studies India

Overview

In the upstream oil and gas industry, digital transformation is changing how business is performed. Digital transformation of E&P proprietary data could save the industry as much as $1 billion each year while boosting production up to 8 percent. E&P data stored in warehouses on various old media technologies cannot be accessed on today’s platforms, and that signifying missed opportunities. Oil and gas organizations possess a wealth of subsurface information. Optimal use of E&P data differentiates production plateaus from fresh income-generating insights using historical data. Companies can forego around nearly 8 billion dollars a year in non-productive time as their geologists, geophysicists, engineers, and data managers spend up to 70 percent of  time filtering mountains of well (onshore and offshore) cores, logscans, and subsurface seismic data in many current and defunct software applications and databases.
Storage, management and organization of giant volumes of hard-copy proprietary records and media archives, and locating and accessing the specific data required for analysis, interpretation and processing, is unwieldy and challenging. This inefficiency renders only 1% of the existing data of any value.

Digitizing and transforming these valuable data into safe cloud-based data, puts priceless E&P information at the geoscientist’s and data manager’s disposal. It is stored privately and securely in the cloud. Digitized geoscience data, when combined with data analytics can be instantly read, interpreted, indexed, manipulated, verified and accessed with smart-tools incorporating artificial intelligence (AI) and machine-learning. Therefore, legacy data can be mined to discover useful patterns for prompt and wise decisions.

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30

Application Processing Days

Under Graduate

Program Level

Fact & Figures

Full Time On Campus

Study Mode

48

Duration

University of Petroleum and Energy Studies

Location

Bachelor of Technology in Applied Petroleum Engineering Upstream Spl in Specialization in E&P Data Management Assistant Fee

$230000

Tuition Fee

$NA

Average Cost of Living

$1300

Application Fee

Bachelor of Technology in Applied Petroleum Engineering Upstream Spl in Specialization in E&P Data Management Admissions Requirements

  • Minimum 50% Marks in Class X and XII. Minimum 50 % in PCM (Physics/Chemistry and Mathematics) in Class XII.
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Detailed Program and Facts

30

Application Processing Days

Full Time On Campus

Program Intensity

Under Graduate

Program Level

48

Duration

Study Visa

In the upstream oil and gas industry, digital transformation is changing how business is performed. Digital transformation of E&P proprietary data could save the industry as much as $1 billion each year while boosting production up to 8 percent. E&P data stored in warehouses on various old media technologies cannot be accessed on today’s platforms, and that signifying missed opportunities. Oil and gas organizations possess a wealth of subsurface information. Optimal use of E&P data differentiates production plateaus from fresh income-generating insights using historical data. Companies can forego around nearly 8 billion dollars a year in non-productive time as their geologists, geophysicists, engineers, and data managers spend up to 70 percent of  time filtering mountains of well (onshore and offshore) cores, logscans, and subsurface seismic data in many current and defunct software applications and databases.
Storage, management and organization of giant volumes of hard-copy proprietary records and media archives, and locating and accessing the specific data required for analysis, interpretation and processing, is unwieldy and challenging. This inefficiency renders only 1% of the existing data of any value.

Digitizing and transforming these valuable data into safe cloud-based data, puts priceless E&P information at the geoscientist’s and data manager’s disposal. It is stored privately and securely in the cloud. Digitized geoscience data, when combined with data analytics can be instantly read, interpreted, indexed, manipulated, verified and accessed with smart-tools incorporating artificial intelligence (AI) and machine-learning. Therefore, legacy data can be mined to discover useful patterns for prompt and wise decisions.

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In the upstream oil and gas industry, digital transformation is changing how business is performed. Digital transformation of E&P proprietary data could save the industry as much as $1 billion each year while boosting production up to 8 percent. E&P data stored in warehouses on various old media technologies cannot be accessed on today’s platforms, and that signifying missed opportunities. Oil and gas organizations possess a wealth of subsurface information. Optimal use of E&P data differentiates production plateaus from fresh income-generating insights using historical data. Companies can forego around nearly 8 billion dollars a year in non-productive time as their geologists, geophysicists, engineers, and data managers spend up to 70 percent of  time filtering mountains of well (onshore and offshore) cores, logscans, and subsurface seismic data in many current and defunct software applications and databases.
Storage, management and organization of giant volumes of hard-copy proprietary records and media archives, and locating and accessing the specific data required for analysis, interpretation and processing, is unwieldy and challenging. This inefficiency renders only 1% of the existing data of any value.

Digitizing and transforming these valuable data into safe cloud-based data, puts priceless E&P information at the geoscientist’s and data manager’s disposal. It is stored privately and securely in the cloud. Digitized geoscience data, when combined with data analytics can be instantly read, interpreted, indexed, manipulated, verified and accessed with smart-tools incorporating artificial intelligence (AI) and machine-learning. Therefore, legacy data can be mined to discover useful patterns for prompt and wise decisions.

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