Top 4 Predictive Analytics Use Cases in the Oil and Gas Industry
The oil and Gas industry is one of the businesses where safety, efficiency, and productivity have to always be in check while keeping costs to a minimum. According to a report by Mckinsey, offshore platforms still have 23% of the production potential that they can achieve which approximately translates into 10 million barrels per day of oil.
The answer lies in the data if you are wondering how to improve the processes and gain more. The data is collected in the upstream, downstream, and mid-stream activities like field assets, rigs, sensors, and more. And if the trends are to be noted then, then oil consumption is only growing over the years.
Source: Our world in data
Therefore, analyzing and leveraging the data that exists is extremely important for asset-heavy industries like oil and gas to increase the reliability and availability of their machines. So today let’s talk about how predictive analytics can help in the research, production, and maintenance of processes.
Predictive analytics applications in Oil & Gas Industry
Though there are quite a few use cases today we are going to highlight the top 4 which are: Predictive Maintenance, Performance Optimization, Safety, and Optimization. Let’s explore them in detail.
Oil & Gas operates on the backbone of multiple machines like drilling rigs, oil compressors, transport equipment, and more. These are highly critical to the functioning and require maintenance checks for good efficiency. There are many forms of maintenance for such equipment like Predictive maintenance, reactive maintenance, preventive maintenance, risk-based maintenance, and more. All these majorly types of maintenance run differently based on the actual product defects or on a scheduled basis, but Predictive maintenance is about continuous monitoring and tracking.
Predictive maintenance is normally implemented for the most critical assets, through monitoring sensor data. With such predictive analytics, you can analyze downtime beforehand and reduce unpredictable, downtown, and reactive maintenance. According to research by ARC advisory group, only 18% of the assets have a pattern that increases with use, therefore with such predictive analysis, you can detect precise changes in equipment that are hard to detect by human eyes.
This results in taking care of red flags well before the asset go into distress, this also can ensure the quality and efficiency of the machinery.
Normally oil and gas operations are done in three areas namely Upstream, Downstream, and upstream activities. Upstream activities include the production and exploration of crude oil, mid-stream activities include the storage and transportation of the extracted oil, and downstream activities include the conversion of crude oil into final products.
Just reading what the process consists of might give you an idea about the possibility of mishaps that would occur and maintenance that would be needed to ensure a seamless transfer of goods. In the downstream process, as the processes would need utmost precision, predictive maintenance would be the best as we spoke about in the previous section. The downstream performance data of multiple machines can be analyzed over time to decide on the end-of-life criteria and failure, this can help to boost asset management.
The midstream data consists mainly of logistics and sensor data to identify any points of leakage, corrosion, displacement, fractures, and more possible areas of loss, this is to ensure that oil and gas are being carried over securely. By identifying the volumetric data across the length of the sensors, you can pinpoint future points of failure. The upstream data from multiple sources and locations can help analyze the seismic activity, and newer sources, and improve reservoir engineering.
One of the other key resources leveraged here is manpower along with the machinery. This is vital because the operations like drilling are handled in remote places and the fumes are hazardous to the health of the workers. So, it is of utmost importance for O&G companies to take care of the safety of the workers. Predictive analytics can combine the accident data from the past to identify any potential red zones, and also any possible current alerts that seem to have a potential error can be identified with ease.
With effective data analytics, you can find the underlying discrepancies and bottlenecks to find out the root causes and eliminate the errors. For example, according to this report by Mckinsey, data scientists found out that operators were using only 20 out of 50 control variables and multiple locations had their own personalized “signature” control settings, leading to variation in production. Secondly, with predictive algorithms, you can predict the possibility of bottlenecks like overflow to reduce pressure buildups and additional storage costs. By creating separate algorithms for the oil content levels based on geography you can ensure that the end product would have the same quality and effective mitigative actions.
For such an analytics project to be implemented, it is important to note that you need to have a proper data architecture and database in place, and also have analytics capabilities and infrastructure to support the Predictive analytics with business-driven agility. By maintaining this checklist you can ensure that you can translate business problems into analytics solutions. With the infrastructure and capabilities that digital transformation organizations have currently, it would be very easy to get the capabilities in case they don’t have them currently.
The O&G companies that are looking to solve the current challenges they have like efficiency, optimization, and more with the help of data analytics and predictive analytics will emerge as the industry leaders of tomorrow.
We’ve discussed above how machine learning algorithms like predictive analytics can help the O&G industry with their upstream, downstream, and midstream operations and predictive maintenance of the entire machinery and operations.
This would be essential for companies to operate at the maximum capacity possible with state-of-the-art advanced analytics, data science, and data management capabilities to get high ROI for the implementation to transform the operations and make the operating model more agile, effective, and efficient.