Information at large

Alick MacGillivray takes a look at the role of Big Data in the energy industry

Big data is the storage, integration and analysis of vast amounts of data that are stored in ‘the Cloud’. Recently there have been many examples reported in both the popular and technical media illustrating the effectiveness and thepossible applications of Big Data. One such example was the successful prediction of the spread of a new virus by analysing what people were searching for on the internet. This was achieved by correlating the frequency of search queries and the spread of the virus over space and time. This required testing of hundreds of millions of mathematical models against actual virus cases – necessitating huge computing power. Considering that the amount of data being stored and the ability to process it is increasing rapidly every year, the use of Big Data to make predictions is only going to increase in popularity and affect more areas of business.

Big Data in the oil and gas industry
This, in turn, begs the question, how does the Big Data revolution apply to the oil and gas industry and specifically to the upstream segment? With the recent reduction in the price of crude oil, operating companies want to increase the amount of oil and gas recovered fromfields by increasing efficiencies rather than by undertaking large amounts of capital investment which they can scarcely afford.

Boosting recovery factors in mature fields using existing assets (known as Enhanced Oil Recovery – EOR) makes much more economic sense than developing new fields using expensive new equipment. To this end, some of the major producers have recently instigated collaborative projects with data analytics providers in an effort to make the most of their data. This initiative also takes advantage of and augments the evolution of the concept of the ‘digital oil field’. The objective of these projects is the following:

  • Gather together all of the disparate data acquired by an exploration and production operation
  • Manage and integrate both structured and unstructured data into a unified set
  • Store the data in the cloud
  • Apply the predictive algorithms and data analytic techniques to the data
  • Identify trends and make predictions that can have a beneficial effect on different parts of the business.

It should be stressed here that the application of Big Data techniques to the oil and gas sector is still at an early stage. So far only a few of the larger companies have adopted the techniques and the full benefits are yet to be realised.

What data are required?
A typical exploration and production operation acquires, stores and processes a vast quantity of data. This encompasses seismic and geological data often acquired during the exploration phase, through to performance data from a large number of sensors used to monitor performance of operational assets. The upstream sector uses sensors measuring quantities such as temperature, pressure, density and flow rate. The capability and speed of these devices has recently increased dramatically as electronics and processing speed has advanced. Other data collected includes GPS co-ordinates, weather information and seismic data. All of this data is being collected every few seconds and so the volume of stored data will increase very quickly.

This type of numerical data is classed as ‘structured’; that is, it can be read by specific software applications or is in a recognised digital format such as OPC. However, typically a significant proportion is classed as ‘unstructured’. This comprises items such as emails, spreadsheets, word processing documents and multimedia. This makes them difficult to store and therefore analyse as part of a larger data set. This can be achieved by using tools that can integrate these diverse data into a unified data set. It is then possible to derive insight into relationships that will be identified when all of the data are processed as a whole.

Where will it help?
Big data analytics can potentially help with a range of different data types from oil and gas. The following areas may derive particular benefit from big data:

Predictive maintenance.
Predicting the time until a device fails can be of help during production operations. This can be done by combining pressure, temperature and flow rates with information data and data on the past history of equipment failure. This is particularly useful if the assets are located remotely where good planning is critical. This type of analysis can also be used to prevent or minimise the amount of operational downtime.

Being able to predict future production levels using past performance data can be used to target assets at areas of highest production. It is also possible to increase field recovery factors by integrating and then analysing seismic, drilling and production data. Big data can help in this way to optimise Enhanced Oil Recovery (EOR), forecast production from individual wells and improve safety. Mature wells where predicted recoveries don’t reach a pre-determined level can be identified and remedial measures taken.

Big data can be used to enhance exploration efforts by using historical drilling and production data. It can also assist in the design of new mathematical models using new computation techniques.

Summary and conclusions
Big Data’s special analysis techniques integrate and analyse data from disparate sources to improve the performance and efficiency of businesses, which has already been used in a range of areas with considerable success. While the use of big data is still at an early stage in the oil and gas industry, some of the larger operating companies have begun collaborating with IT providers to introduce big data analysis to improve their performance. Within the oil and gas industry, areas that can be assisted include production, equipment maintenance and exploration. It is anticipated that the application of big data to the oil and gas sector will continue to increase quickly and that it is likely to play a significant role in driving efficiencies in the near future.

Alick MacGillivray is Senior Consultant at NEL. NEL is a world-class provider of technical consultancy, research, testing and programme management services. Part of the TÜV SÜD Group, NEL is also a global centre of excellence for flow measurement
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