Leveraging Big Data Analytics in the Oil & Gas Industry

The oil and gas industry has seen great tech implementations over the last few decades to gather real-time data from field equipment. But with an ocean of data now available, it can be overwhelming to know how to use that data to your advantage. According to an article published by  ScienceDirect, big data analytics can make significant improvements in the oil and gas industry, but there is a lack of business support and awareness in the industry that is hindering the wide-spread implementation of big data analytics.

Due to advancing technology, data science has the potential to reduce or eliminate some of the persistent challenges facing the oil and gas industry in each sector; namely, safety in upstream operations, optimization of midstream operations, and improvements in downstream operations. Big data analytics could be a huge turning point for those looking to increase operational efficiency, reduce operational expenses, and increase the speed and accuracy of making decisions.  

Big Data Challenges

The technological advancements in the oil and gas industry means there is an increase in incoming data. Big data is larger, more complex sets of data that are coming from multiple sources, as explained by OCI. A significant concern is whether oil and gas companies are equipped to handle the amount of data coming in from new sources. Data volume, quality, integration, and processing are all variables that can challenge the effectiveness of data analytics. Big data relies on technology to process raw data and experts to understand the analyzed information being generated, not to mention handing that knowledge off to companies to make informed business decisions.

Another concern and challenge with big data is cybersecurity. If the oil and gas industry wants to commit to using big data, ensuring its integrity must be paramount when that data is used to make critical business and safety decisions. Maintaining confidentiality is also important for businesses to stay competitive in the market. Having more data increases the potential for cyber attacks or leaks if not properly protected. 

Finally, the lack of support from business leaders can be problematic for those wanting to invest in big data analytics. CNBC confirmed that less than 1% of data collected from the field is actually available in a usable format for those making business decisions, which indicates that oil and gas companies are significantly under productive by failing to utilize big data analytics. Many business leaders don’t believe this technology applies to them or that it costs too much (EAG). However, data analytics is the best way to effectively use raw data collected from the field, process it into meaningful information, and then make well-informed decisions. 

Benefits & Use Cases of Big Data Analytics

Before taking the plunge to utilize big data, one must understand how it can benefit oil and gas companies specifically. In the three sectors of oil and gas, upstream, midstream, and downstream, there is a significant amount of data being collected without the corresponding tools to analyze the wealth of information gleaned from each of these sectors. By leveraging big data analytics for the benefit of oil and gas companies, the outcomes show improved decision making, optimized operations, and efficient management of data.

Big Data for Upstream

In the upstream sector, advancing technology is generating huge quantities of data from sources like seismic monitoring, LWD and MWD, channel counting, and more. Big data sets compiled from this information have little value without being processed and analyzed through data science, and it’s utilizing this method that can generate long-term improvements. This sector has high potential to leverage big data analytics because finding trends among big data sets can help increase production rates, improve exploration efficiency, and reduce risks. The overarching problem in all areas of oil and gas is the unused potential of raw data. 

In regards to drilling, employees and leaders rely on real-time information gathered and processed through data analysis to make good decisions. This tool gives companies the ability to optimize drilling projects, from determining locations and improving safety to reducing drilling costs and risks. The small percentage of data companies utilize are often applied only to immediate decisions, like improving the efficiency during a specific drilling job. But there is huge potential for making better informed long-term decisions by monitoring data across a longer period of time to find trends that point to sustained solutions.

Big data analytics has also been used to build reservoir management applications based on the numerous sources of monitoring in reservoir management, like temperature and acoustic sensors. The information by itself presents a current picture of reservoir status, but a new reservoir simulation technique (ScienceDirect) using machine learning and data analytics can predict more effective parameters. The reservoir models produced can mitigate uncertainties surrounding reservoir performance. 

Big Data for Midstream

Midstream operations can benefit from data analytics to improve pipeline integrity and maintenance, optimize logistics, and increase supply chain efficiency. Deciding where to build pipelines, how to build them, and how to keep employees safe while maintaining product and pipeline integrity is full of information that can be analyzed to show the best options for each of those concerns. 

On the logistics side, there are many factors that play into a company’s effectiveness, and using data analysis to determine the best way to optimize decision-making in the midstream area can free up time and energy for other areas. Time is money when it comes to transportation, and data analytics can help automate a lot of midstream tasks and further optimize company operations. 

Big Data for Downstream

In downstream operations, some of the challenges of this sector are achieving operational efficiency, market demand forecasting, and managing production costs. Companies that use big data analytics can develop a workflow to improve the efficiency of complex processes in refineries and indicate what the market will look like for oil and gas products. Managing optimization and efficiency for refining and processing will overall reduce production costs and safety risks.

Specifically within refineries, oil and gas companies can make good use of data analytics by compiling safety monitoring and regulatory compliance information to develop safety predictive analytics. Hardware implementations over the last decade do a great job of bringing in data, but that data is often unusable, so your environmental engineers are spending hours of their time manipulating it into spreadsheets. A good software can process the big datasets to note outlying information and better predict potential hazards. 

Additionally, automated reports generation could take that data and generate regulatory reports for submission to the government.

Conclusion

As detailed in this article, big data analytics has the potential to significantly improve and progress companies in the oil and gas industry across all sectors. It can also help reduce or eliminate the challenges facing the oil and gas industry currently.

Those aiming to find a software development team that can help reach their company goals should look no further than EnterBridge Technologies. We can provide custom software solutions that harness data science for the benefit and advantage of your company. Book a call with us today.

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