In 2017, McKinsey in its article titled ‘Why oil and gas companies must act on analytics,’ reported that an offshore platform usually achieved 77% of its production potential when the industry was struggling with a shortfall of 10 million barrels per day creating a $200 billion performance gap. The operational complexity of production and processing facilities were cited as the main reason behind this wide performance gap.
Come 2020 and the world witnessed a historic collapse in demand resulting in ample supply surplus.
But at the same time, it also disrupted the oil production, bringing it down from 100.5 million barrels per day to 93.9 million barrels per day.
Due to increasing uncertainty over demand growth in the coming years, governments and oil companies are hesitant to build new capacity while curbing investments, delaying projects, and withdrawing resources from the ground. What does that mean for the industry? It means a greater need to keep operations up and running by maximizing existing assets and optimizing production.
By adopting advanced analytics, oil and gas companies combine the power of engineering, data science and AI that in turn enables them to achieve a number of benefits in the face of disruption. Let’s take a look at the four key use cases of predictive analytics:
1. Enhanced Maintenance
Oil and gas companies operate with a huge and varied set of critical assets ranging from the offshore pumping station, drilling rig, pipeline booster station to compressor or transportation equipment, across three key areas-upstream, midstream and downstream.
These complex and critical assets demand continuous checking and monitoring, generally from a remote location. Access to real-time health data of assets as well as performance insights can help operators to form informed, timely decisions to mitigate any possible risks, drive efficiencies, and gain competitive advantage.
Predictive analytics offers five approaches that can be taken to enhance the assets health and performance maintenance for oil and gas organizations:
|Reactive maintenance (RM)
|Allows an asset to run till it drops
|Non-critical assets with zero impact and replacement cost
|Preventative maintenance (PM)
|Processes maintenance related data to perform analytics on an asset to drive efficiency and asset availability
|Assets having critical operational function or failure modes that regular maintenance can prevent
|Condition-based maintenance (CBM)
|Monitors the present condition of an asset to determine maintenance needs
|Measurable parameters act as good indicators of impending disruptions
|Predictive maintenance (PdM)
|Provides advanced warning of impending failures by monitoring performance through sensor data and prediction engines
|More complex and critical assets that require round-the-clock monitoring
|Risk-based maintenance (RBM)
|Empowers operations and maintenance personnel in decision-making using PdM, CBM and PM insights.
2. Identification of Issues
A 2015 research showed that only 18% of asset failures were caused by reasons that increased with use or age. This means preventive measures alone are not capable of mitigating such risks. By combining predictive analytics with predictive maintenance (PdM), operators can identify such unseen issues in assets.
By leveraging predictive analytics, operators can keep a track of operational signatures of every critical and non-critical asset and compare the same with real-time data in order to detect the slightest changes in asset behavior. As a result, maintenance personnel can take corrective actions way before the traditional alarm goes off.
3. Optimized Health and Performance
Predictive asset analytics solutions alert oil and gas companies about equipment issues and failures in advance, sometimes days, weeks, or months before, giving them sufficient time to take corrective measures and avoid unscheduled downtimes.
This proactive approach, in turn, helps operation and maintenance resources to plan better and reduce maintenance costs in the long run. It also increases asset utilization and identification of non-performing assets (NPAs). In a nutshell, by investing in predictive analytics, oil and gas corporations are able to improve profitability and increase asset availability.
These analytics solutions also come with a faster and smarter capability to capture and transfer knowledge in real-time ensuring comprehensive and timely decision-making even when faced with transitioning resources.
4. Smarter Operations
This is the age of IoT, a technology that is ramping up business value by enabling faster and smoother integration of multiple equipment creating a huge pool of data. For oil and gas companies, this is both a challenge and an opportunity as this vast data pool can be leveraged to mitigate risk and boost productivity.
Predictive analytics enables these companies to comprehend and ascertain the current conditions of an asset that helps in measuring the overall impact of performance deficiency, assessing the consequences of asset failures, and reducing CapEx and OpEx.