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Making the most of digital oilfields

By Peter Fitzgibbon - 25th October 2014 - 09:27

As the energy giants prospect in ever more geologically difficult locations, Phoebe McMellon explains how Digital Oilfields (DOF) technologies are being adopted to extract meaningful information from Big Data

Digital Oilfield (DOF) technologies have become increasingly popular with oil and gas companies, as tools to manage the flood of data being collected every day. Early adopters include oil majors such as BP, Chevron, Shell and Statoil, among many others1.

Digital Oilfield is the term used for technology-centric solutions, many of which take advantage of big data, allowing companies to manage their operations with greater efficiency. For instance, DOF technologies can help geoscientists to quickly and accurately analyse dig-data generated by oilfield equipment monitors, like down-hole multiphase sensors, measurement-while-drilling applications, multilateral completions and down-hole separation.

DOF technologies are especially useful in cases where the geology is complex, the exploration area is difficult to access (e.g., the deep waters of the North Sea or Gulf of Mexico) and human resources are limited.2 In such locations, DOF technology is used in the early stages of drilling to optimize the identification of the most productive and economically viable reservoirs hidden in complex subsurface geologic strata.

These technologies are also used in later stages of field development. On the production side, the technology is used to reduce the number of human resources required onsite, increase the percentage of natural resources extracted and amount of BBOE produced, and minimize health, safety and environmental risks.

It is not surprising, therefore, that exploration companies operating in previously unexplored and more geologically difficult locations like the Red Sea and the eastern Mediterranean Sea are looking towards big data.

Cherry picking actionable information

DOF technology employs specialised algorithms that allow geoscientists to cherry-pick actionable information out of real-time data. It also standardises technical terms across databases to ensure that geoscientists, geophysicists and drilling operators are all sharing common data with common language. This reduces uncertainty and costs across exploration and production and is producing noteworthy results. In particular, oil and gas companies are using supercomputers to store and analyse huge clouds of data generated by 3D seismic imaging. This data is being used to discover new plays and reservoirs in locations previously explored using more primitive data information approaches.

This technology empowers the oil and gas industry in many ways. In the case of exploration, DOF software and supercomputers can virtually integrate all of the data required for exploration. It can filter and organise data to provide the strategic, tactical and operational information that decision makers require during the entire lifespan of the project.

Meaningful picture

One of its chief merits is that it enables users to process disparate pieces of information, which alone may have little or no meaning, to create a meaningful picture. Moreover, if a whole field is being monitored, then individual well data performance from several rigs can be integrated to provide a more accurate picture of subsurface activity in real-time; improving efficiencies even more.

DOF technology, with its ability to generate clear evidence-based data, is no longer hypothetical, but a crucial ingredient in building a sound business case for drilling a well. This is justifiable not only to geologists, but key decision makers in the company, as well.

1Lo, C. (2013, June 10). Making the most of the digital oil field. Retrieved from

2 PRWeb (2014, August 6). Digital Oil Field Technology Market: Global Industry Analysis, Size, Share, Trends, Analysis, Growth and Forecast 2013 – 2019. Retrieved from

Phoebe McMellon is Director of Product Development for Geofacets, the innovative map-based research tool from Elsevier (

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