Introduction to Big Data Analytics in Oil and Gas

Oil and gas is a data driven industry. Whether it is in upstream, where data form an integral part of exploration and drilling program, or in using smart metering and supervisory control and data acquisition (SCADA) systems in midstream, the industry heavily depends on IT and data analytics to increase the speed of finding oil, enhance oil recovery, and reduce health, safety, and environment risks that arise due to equipment failure or operator error. Over the past few years, a new technology based on commodity hardware (Big data and analytics) has been in place for processing large amount of complex data for reservoir modeling or simulation. This new technology can process high volume of complex data with different scenario and access patterns at relatively less time than conventionally available technology.

In oil and gas industry, big data can from traditional sources such as equipment monitoring and maintenance records. However, until recently it was not used for long-term application. The potential for Big data and analytics lies in accessing the amount of new and untapped data, thereby enabling the use of data across disciplines (geology, petroleum engineering, accounting, and so on). This helps the personnel to gain access to unlimited searchable knowledge, while helping them find what they are looking for more quickly.

Significance of Big Data Analytics in Oil and Gas Industry

Oil and gas companies exist in a rapidly changing environment. There are many external challenges which an oil and gas company can face such as new methods for extracting energy, alternative forms of energy that can enter the market and create an oversupply, and so on. Moreover, political disturbances too can create shortages. While facing such uncertainty, many companies place emphasis on better asset management and control. Apart from maintaining effective and efficient oil and gas exploration it is important to have favorable environment track record and maintain good public and government relations. In addition, gaining government approval for new exploration and production remains top priorities for good business scope.

Exploration and Refinement Effectiveness

Since ages, oil and gas companies have focused on improving exploration effectiveness through advanced analytics applied to variety of data. Seismic data are used to take decision on drilling activity. In today’s challenging environment, cost of new exploration must not increase while success rates need to improve to maintain profitability. Similarly, increasing reservoir capacity and containing costs are major factors in maintaining profitability.

Such data have been traditionally analyzed in data warehouse consisting of relational databases. Today Hadoop clusters are used partly due to their low cost and partly due to their Schema-less file system, which is ideal for predictive analytics.

Operational Efficiency

It is companies’ top priority to achieve operational efficiency from drilling sites. These data are gathered from sensors providing real-time information on the state of operations. By monitoring the changing state of keep components, one can better understand when maintenance is required. Moreover, effective operation helps to have greater efficiencies and cost savings. By implementing predictive analytics across big data management system, one can increase overall safety as well as reliability and reduce costs. Such analysis can pin point potential safety issues or identify environmental risk.

Predictive analytics can also be implemented in supply chain management system and data warehousing, such as for routing vehicles, crew, and supplies to exploration and production facilities. Such routing is important in providing lowest cost of delivery while maintaining profit margins. It is also critical for delivering parts on time, thus possibly avoiding major maintenance and safety issues.

Understanding a Changing Environment

Oil and gas is very unpredictable industry and price volatility can have serious implications on the companies’ bottom lines, yet their values during the entire life of project can determine whether exploration, production, and refining make financial sense.

Predictive analytics can play a significant role in determining the direction in which prices are moving, thereby identifying the right exploration and production level. Moreover, such data can be used to take timely investment decisions.

Deploying Big Data Analytics in Oil and Gas Industry

Big data analytics has applications across the entire value of oil and gas industry – from geology and exploration to production and operations, transport and refining, and retail.

Exploration and Development

While conducting exploration for new resources, big data and advanced analytics is used to perform “identity traces”. Combination of these two techniques can help in identifying previously overlooked but potential productive seismic trace signature. Big data and analytics can be applied to other advanced explorations too. With the help of these, data scientist can verify assumptions when new surveys are restricted by environmental regulations. Similarly, information on weather patterns and ice flows can help analysts make connections with operational processes, such as impact of storm on rigs. Other areas where big data and analytics can be applied in oil and gas industry are in enhancing searches and assessing acreage and generating new prospects.

Drilling and Completions

Using Big data and analytics to identify possible interruption in drilling activities is of great interests to the service providers. Analyzing large amount of data to identify conditions or anomalies in drilling activities can save millions of labor and equipment costs. Meanwhile the data collected by SCADA system  from the wellheads can be utilized to maximize asset performance and optimize production.

Production and Operations

Big data and analytics are of great importance to production and operations. Big data help in predicting the future performance of the assets with the help of historical data. With the help of this analysis specific zones can be identified which do not perform well and necessary actions can be taken, either to move the asset or shut it down. Oil recovery rate can be improved as well, by integrating and analyzing seismic, drilling, and production data to provide self-service to the reservoir engineer.

Enterprise Security

In the recent past, oil and gas companies have invested heavily on security, deploying a wide range of technologies to protect their intellectual property. Big data and analytics can be used to manage internal network threats. Correlating network events with metrics and identifying patterns to predict cyber-terror is critical, particularly in light of recent events at a refinery in Middle East.

With enhanced security and operational monitoring, oil and gas companies are beginning to apply analytics to find out anomalous information patterns on the network to find out intrusion decision. By using predictive analytics, companies can now anticipate IT security breaches and are bolstering security with data from video monitoring.

Future Outlook of Big Data Analytics in Oil and Gas Industry

Oil and gas companies are in constant endeavor to invest in information technology to improve their existing operations.
For oil and gas companies Big data analytics will have to provide value by:

  • Increasing speed of first oil
  • Enhancing production
  • Reducing risks, especially in the area of health, safety and environment
  • Reducing costs, such as nonproductive time

Big data analytics can be applied to various areas apart from those mentioned above, such as micro seismic and leveraging untapped and unstructured data. Big data and analytics can be used across disciplines (geology and geophysics, reservoir engineering, production engineering and so on). The idea is to manage multiple data sources across various disciplines and bring together data that have not been analyzed, which can provide new insights to the operators. Big data along with collaborative technologies can provide personnel with vast searchable information that do not require expert manpower. Furthermore, it will help the operators to get an accurate data more quickly, as a lot of time will be saved in accessing and loading data.

Conclusion

Big data and analytics is expected to play a significant role in shaping the future of oil and gas industry. By using this companies can gain new insights, thereby enhancing their business values that helps in improving bottom line and leads to having competitive advantage. In order to make the effective use of large volumes of data, companies must implement new strategies that will help them in mining these data more accurately and quickly and use those to make smart decisions. Moreover, the companies need to invest in latest and upgraded technologies to support their big data initiatives. As a result, throughout the oil and gas industry companies are heavily investing on big data analytics across their value chain, to pursue new business opportunities, reduce costs, and streamline operations.

About Frost & Sullivan

For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success.

Frost & Sullivan

For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success.

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