Data: Value Creation in the Digital Era

Real time data capture and Internet of things have enabled data collection at every step in the O&G industry. We have data for each and every bit of operations and equipment for every 1-2 seconds in most cases. As a result, every player in the industry ends up with huge volumes of data in their databases which does not necessarily yield any value unless and until they have a sound data collection, storage and utilization strategy around it.

However, things can look very different with minor additions to the way they handle their data. Advanced Analytics and Machine Learning led solutions can enhance the value coming from data after it has been quality checked, aggregated and stored in an orderly manner for an integrated view and easy retrieval.

The first and foremost value data brings is through Insights it generates. Comparing a variety of datasets can yield some startling revelations for example, using a specific separator could be increasing flaring and making the operator prone to compliance penalties or a particular vendor could be the most unprofitable one inspite of them having a history of collaboration with the operator.

Secondly, Data can be used for Targeting, which essentially means understanding recurring problems and planning for the resolution differently. One example I can think of is finding that a particular brand/ specs compressor fails more often than the rest of the compressors and working on a different maintenance schedule for them.

Data can also be used to personalize needs. for example, in a remote surveillance system we can propose different home screens for different personas. A production engineer would prefer starting with an GIS based integrated view of the wells while a maintenance engineer would like to start with a tabular display of equipment with performance parameters shown clearly. Such a small personalization also goes a long way in promoting adoption of such tools in field operations considering the low adoption rates of digital at the operator and engineer level in Oil and Gas.

Lastly, Data can also be used to contextualize any specific needs of a given user. For example, a drilling engineer might be able to contextualize collected drilling operations data to spot a list of expected problems at certain depths and formation composition. This is purely based on the system using the inputs to compare with a wider set of data sets collected from the same field as well as other wells having a similar profile and specs.