Data analysis has been an intrinsic part of the Indian oil and gas (O&G) industry for almost four decades now, with the upstream segment of the industry using sophisticated seismic software, visualisation tools and other digital technologies to analyse large sets of data and make data-driven decisions.
Major city gas distribution (CGD) players are also considering adopting technologies such as SAP and enterprise resource planning (ERP) to understand and analyse large sets of data in real time. These will enable them to undertake advanced data simulation and analysis for multiple scenarios, real-time data update from the field to the back-end ERP system and radio frequency-based meter reading/automatic meter reading. Technology will not only help the industry in improving capacity utilisation but also boost operational efficiency while minimising losses and enhancing operational safety.
Need for big data analytics
Engineering equipment in the O&G industry is generating large quantities of structured and unstructured data on a daily basis. In such a scenario, if companies adopt machine learning and big data analytics, they can better analyse the structured data sets and make intelligent decisions.
The quantum of data is expected to only grow going forward. Thus, it becomes imperative for companies to invest in big data analytics to boost production and returns on investment, as well as manage safety and risks on a real-time basis. As per the 2017 Digital Refining Survey conducted by Accenture, new digital technologies have already become a top investment priority for O&G refiners in the US. Similar trends would emerge in the Indian O&G industry as well as in the medium to long term.
Expected disruptions due to big data
Big data is expected to disrupt the O&G industry significantly. For instance, a major disruption could occur in the downstream retail CGD network wherein a self-driven car would visit a prepaid refuelling station on its own at midnight and replenish fuel for taking its owner to the office the next morning.
For the upstream industry, exploration and production (E&P) companies operating in unconventional fields can pair their real-time, down-hole drilling data with the production data available from nearby wells, which will help them correct their drilling strategy accordingly. This real-time feedback could completely disrupt the way the upstream O&G industry functions.
Why make the analytics shift
As per a recent survey conducted by Ernst & Young, about three-quarters of the 75 large O&G companies surveyed in the US are planning to allocate 6-10 per cent of their annual capital budgets for the adoption of digital technologies. Indian majors are also following suit and planning significant investments in big data analytics. For instance, ONGC is planning to engage data scientists from various Indian Institute of Technology (IITs) and IT firms over the next few months. They will help ONGC develop a big data platform containing all E&P data such as geological, seismic, well log, drilling and output figures at one place. It will enable ONGC to optimise its operations and boost productivity from its O&G wells.
With the fundamental shift in data-based decision-making, companies need to invest in big data analytics to:
- Understand and analyse critical sets of data: Structured and unstructured data sets provide critical information, which can prevent any repetition of past failures in the future. For instance, a pipeline operator can learn from a previous incident and avoid a repetition of the same in the future.
- Predict any anomalous conditions: New machine learning algorithms are being developed every day. These can predict any imminent risks to the integrity of O&G pipelines based on the data being fed into the algorithms from the associated engineering equipment.
The way forward
Big data is expected to disrupt the O&G gas industry in the medium to long term. Companies need to move ahead on their maturity model and engage in big data analytics to undertake more intelligent decision-making. The Indian O&G industry has been comparatively slow in adopting big data analytics and machine learning as compared to their global peers. However, Indian players are now increasingly considering the adoption of big data analytics to boost their productivity and undertake more informed decision-making.