Big Data Analytics in Oil and Gas Industries

The Oil and Gas industry, a sector known for its rapid adoption is opening door for cutting edge big-data analytical technologies in order to improve exploration and production. This industry is divided into Upstream, Downstream and Midstream divisions. Upstream division is concerned with exploration, development and production of crude oil or natural gas. Most upstream work is in an oil field or an oil well which can be offshore or onshore, oil sands or oil shale. Areas where analytics could be used are following:

  1. Exploration, development and drilling optimization.
  2. Production optimization
  3. Risk assessment and modeling
  4. Predictive asset maintenance and availability

When exploring for new resources, analytics can be used to perform “identity traces for identifying previously overlooked, yet potentially productive seismic trace signatures. Other forms of advanced exploration become possible, too. Relying on historical drilling and production data from local sites, for example, can help scientists verify assumptions when new surveys are restricted by environmental regulations. Similarly, reviewing information, such as weather patterns and ice flows, from data marketplaces can help analysts make connections with operational processes, such as the impact of storms on rigs.

Being able to predict future performance based on historical results, or to identify sub-par production zones, can be used to shift assets to more productive areas. Oil recovery rates can be improved, as well, by integrating and analyzing seismic, drilling, and production data to provide self-service business intelligence to reservoir engineers. Other examples of analytical applications are:

  • Preventing down time. Understand how maintenance intervals are affected by variables such as pressure, temperature, volume, shock, and vibration to prevent failure and associated downtime.
  • Optimizing field scheduling. Use this insight to predict equipment failures and enable teams to more effectively schedule equipment maintenance in the field.
  • Improving shop floor maintenance planning. Integrate well and tool maintenance data with supply chain information to optimize scheduling of shop floor maintenance.

Other applications in upstream division can help identify exceptions, anomalies, trends, patterns and relationship in the data. For example oil and gas companies can visualize and identify production trend over different geography and different times. They can measure injection influence and find anomalies such as water break-through and find what are the critical operating factors for work are over and infill drilling candidates, estimating reservoir quality and production potential, find the best infill drilling locations.

As oil and gas companies work to optimize operational efficiencies, they also must strive to improve their drilling strategies to increase the success of extraction. Being able to use big data volumes to identify conditions or anomalies that would impact drilling can save millions in labor and equipment costs alone.  Related areas where analytics can enhance geoscience include:

  • Leveraging scientific models. Incorporate geologic measurement and scientific models into everyday processes, such as shale development.
  • Improving engineering studies. Engage sophisticated subsurface models and conduct detailed engineering studies on wells to identify commercial prospects earlier and with less risk.
  • Optimizing subsurface understanding. Use big data tools to understand the earth’s subsurface better and to deliver more affordable energy, safely and sustainably.

Oil and gas companies must also manage increasingly complex refining and manufacturing processes. To help increase worker safety, streamline regulatory compliance, improve the longevity of assets and enable rapid responses to problems, organizations need efficient ways to manage and provide access to accurate information about the current state of equipment, facilities and processes. They need to Improving the safety and maintenance of facilities and increase lifespan of equipment by implementing condition-based maintenance using big data driven predictive analytics.

Oil and gas companies must also manage increasingly complex refining and manufacturing processes. To help increase worker safety, streamline regulatory compliance, improve the longevity of assets and enable rapid responses to problems, organizations need efficient ways to manage and provide access to accurate information about the current state of equipment, facilities and processes. They need to Improving the safety and maintenance of facilities and increase lifespan of equipment by implementing condition-based maintenance using predictive analytics.

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