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Data Radically Shifts the Way Oil & Gas Industry Approaches Operational Efficiency

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Click to learn more about author Chirag Shivalker.

With reducing prices in the energy sector, oil and gas companies are required to focus more on bringing in higher operational efficiency. For this, predictions are made that Data Management and analytics will lead the digital journey for the industry. Capital investments in energy projects have doubled since the year 2000, and are likely to grow $2 trillion annually by the year 2035, so accurate cost predictions as against the benefits is mandatory.

Oil and Gas Industry and Digital Transformation

If we look a few years back, oil and gas industry was an early adopter of digital transformation. Approximately 50 years back, it was the first industry to use digital distributed systems (DDS) to take charge of refineries and other downstream plants. Then came in the digital oil field concept, and made it a leader in adopting digital representatives of seismic data for representing deposits and reserves. The challenge for the industry now is to implement the next wave of Digital Transformation and radically shift the way it approaches operational efficiency with help of data and analytics.

But crude price fluctuations & other unpredictable outside variables have pushed capital project planning towards uncertainty, making large-scale projects face significant overages. If performances of such gigantic projects are measured, they are no less than horror stories. The root cause of the nosedive, as project teams confess, is the amount of information that they have to understand and assimilate to make right decisions at the right moment. Apart from these factors; newer geographies, regulatory requirements and competition are compelling O&G industry to renew investments in technology and processes to increase efficiency and profitability.

The industry is supposed to be equipped adequately to address real-time Data Management as an assortment of activities such as effectively capture, store, manage, and analyze data. Oil and gas datasets are a critical alternative data resource. Oil and gas companies need curated, up-to-date oil well, operator, and production data for investment decision-making.

Innovation in Oil and Gas

Gone are the days when it was synonyms to developments in hardware leading to bigger, faster, deeper drilling, or more powerful pumping equipment. If not these, it further goes down to bigger and better transport, unconventional wells, hydraulic fracturing, horizontal drilling, and other enhanced oil recovery (EOR) techniques. Just like any other industry, Oil and gas also is in dire need of optimization, and size of capital investments and the high cost of errors makes it all the more important than other industries.

Oil and gas industry deals with a large amount of upstream, midstream and downstream data; and since long if compared to others. Upstream data is all about exploration, discovery, land and sea drilling, and production. The midstream data encompasses transportation, wholesale markets and manufacturing and refinement of crude. And downstream data talks about the delivery of refined products to the consumers. The benefits of Data Science, Machine Learning, Predictive Analytics, and various other advances in digital imaging and processing have driven innovation to create a rich and disruptive movement among oil and gas companies.

Increase in quantity, resolution, and frequency of seismic data, and data generated by advances in the “Internet of Things like attached sensors, devices, and appliances are getting integrated with existing data, is what we are referring to.

  1. Exploration & Discovery of Oil and Gas

During oil and gas exploration 2D, 3D, 4D seismic monitors generate a huge amount of data which if leveraged correctly, helps in finding new oil and gas fields. It also helps in identifying potentially productive seismic trace signatures – previously overlooked.

Putting at task Data Management expertise for activities right from data collection to Analytics has proven its worth in providing rapid and accurate insights. Considering data variables that impact the profitability of oil rigs across production costs, transport of oil, employees, weather-related uptime or downtime and many more; Data Management plays the key role.

Monitoring and analyzing the massive amount of drilling data in real-time and alerting about variances that occur based on different variables such as weather data, soil data, equipment sensor data etc., is also very much possible. It helps in predicting the success of drilling operations in real-time, especially when looking for new oil or gas.

Seismic data also turns out to be really useful for determining the amount of oil and gas in a new or previously overlooked oil well, but only when the data is analyzed by experts. Historical production numbers and drilling data from local sites gives additional insights about future production volumes. This benefit comes in really handy when environmental restrictions prevent your oil and gas company from conducting surveys. Combined with public data such as weather data, ocean currents or ice flows an accurate prediction can be made regarding future production volumes.

Sensors placed within oil wells and across the earth surface provides additional information about seismic activities and how drilling affects it. The closer, are sensors located to the seismic activities, the earlier seismic activity gets detected and warning can be sent to all concerned about a possible earthquake.

  1. Optimization of Oil and Gas Production

Data collected from myriad sources such as sensor data from equipment including pressure, temperature, volume, shock, and vibration data can be used to detect errors if any or upcoming failures during the drilling process. Geological data including scientific models related to the understanding of earth subsurface and weather data can be used to ascertain and predict the impact of storms on the oil rig.

Data collected through sensors attached to drill-heads and other equipment can be analyzed to understand how the equipment is behaving and why. It also helps in predicting when an equipment or machine is about to fail, when and what type of maintenance will be required. Combining new and historical data of equipment failures makes it possible for the oil company to monitor all their equipment around the world, in real-time, to predict equipment failure.

Data collected and analyzed centrally will provide the opportunity to better understand which equipment works best in what environment, ultimately enabling organizations to optimize how the equipment is used to reduce latency. Combining sensor data with the ERP of an organization, new spare parts can be ordered well in advance, and even before the machine fails. It also helps engineers to schedule and plan maintenance, and adjust it accordingly to reduce downtime and inventory levels.

  1. Mitigate Risk and Ensure Safety

Using data collected from various sources for Analytics and using it to detect, in real-time, to make decisions early on to shut down the plant if necessary to prevent any large mishaps or environmental risks, is very much possible. Video surveillance data captured by smart cameras are effectively utilized to keep an eye on the proceedings of the entire oil plant. Algorithms can be used to detect patterns or outliers and predict security breaches online and offline both, while also can alert security to take actions against security breach at sites, globally.

Final Word

Things have, and are consistently changing tremendously in the oil and gas sector, where cluster compute platforms, massive yet cost-effective storages, and new techniques are enabling oil and gas companies to improvise and elevate their existing tools and methods. Data Management as a group of activities right from data collection to data entry, and data processing, categorization, and validation; are being adopted and practiced across the industry.

However, there still are several companies which lack the required Data Science know-how and resources.  With immense expertise in geosciences and engineering, they fall short of unstructured data, Machine Learning, Predictive Analytics, Artificial Intelligence, and various other Data Science specific expertise and experience too. Some of the oil and gas related companies have identified the need of the hour and building out Data Science teams internally, but a majority of players in the oil and gas industry are taking up the most viable option of turning to outside companies for Data Management expertise.

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