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Rationalizing your Oil & Gas Assets through AI, BI and Big Data

Meera Office's picture
Marketing Manager Target Solution LLC

Project manager for the oil and gas energy sector for about 10 years. Working with TARGET solutions LLC.

  • Member since 2020
  • 1 items added with 2,548 views
  • Jul 7, 2020

Artificial intelligence (AI), business intelligence (BI) and big data are the terms that are getting more popular with each passing day. In enterprises, AI and BI are usually misunderstood. Talking about artificial intelligence, it is more about finding ways of replicating human attributes such as learning, predicting, problem-solving, and judgment.

Business intelligence (BI) focuses more on analyzing business data with the help of various tools and technologies.

Whereas Big Data or Big Data analytics is a very huge collection of structured, unstructured, and semi-structured data that organizations can mine for information.  Big Data comprises of few main characteristics that include volume, variety, velocity, veracity, value, and complexity. Organizations are using big data in machine learning projects, analytical applications, and predictive modeling.

As in the Oil & Gas sector, most of the business actions are based on data that comes from multiple resources which implies that this sector has a huge potential for AI and big data. Oil & Gas companies can benefit from the combination of these three with the help of BI companies in data processing for related insights. These technologies can help the Oil & Gas industry in enhancing business value by:

  • Optimizing production and exploration
  • Minimizing downtime
  • Understanding and modeling of reservoir

AI, BI, and Big Data can be helpful in all the above-mentioned areas of Oil & Gas but can be proved to be most useful during exploration and production.

Different aspects of the Oil & Gas industry that can be improved with the successful implementation of big data are:

  • Analysis (when the exponential growth of storage is manageable)
  • Data Availability (incase data loss can be evaded)
  • Development and exploration
  • Drilling  (to meet completion deadlines)
  • Productions and operations
  • Security and safety

Although Big Data is getting popular in E&P companies, however, there are still some key challenges that need to be addressed for applying Big Data efficiently. Awareness about Big Data within the Oil & Gas industry, lack of business support, and quality of the data are a few challenges that need to be considered.

The dynamic reservoir simulator is supported by AI-Physics. Artificial intelligence, machine learning, conventional simulation techniques combined with business intelligence, enable fast production forecasting, and history matching.

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