To keep up with the ever-changing influence of weather and environmental parameters on their operations, businesses are now using adaptive cognitive computing in oil and gas.
AI is already transforming the oil and gas industry with its various applications. But AI is majorly used to automate repetitive tasks and processes such as drilling, customer service using chatbots, and equipment and reservoir inspection with AI robots. However, it cannot be used for tasks like reservoir analysis, exploration, and complex decision making. And that’s because the data about each well and minerals within it keeps on changing constantly based on the location and other environmental factors. Most AI systems cannot quickly adapt their analysis to these changing factors that impact oil and gas exploration. That’s where cognitive computing can help. Applications using cognitive computing in oil and gas can quickly adapt to changing environmental factors and provide suggestions accordingly. Some of the key features of cognitive computing that differentiate it from AI is quick adaptability, personalized intractability, and context-based understanding. With these features, cognitive systems can enable machines to enable better human intelligence than other AI systems. These features of cognitive computing have paved the way for its various applications in the oil and gas industry.
Applications of Integrating Cognitive Computing in Oil and Gas
Cognitive systems can adapt to changing weather and environmental conditions. And they can understand the context of business situations to assist in making better decisions for reservoir exploration and optimal mineral production.
Facilitating Smarter Decisions
Mergers and acquisitions are a common occurrence in the oil and gas industry. Businesses merge with and acquire other organizations or reservoirs for growth or due to a lack of investment funds. But before acquiring or merging with other businesses they need to make sure that they are investing in the right reservoir. Hence, they need to analyze data about the reservoir to make better decisions. Geo-location and environmental factors of each reservoir make them unique. And this makes it difficult for analytics technologies to learn from historical data and analyze each reservoir independently. Cognitive computing systems are adaptable to changing data and can analyze a large volume of structured and unstructured data. This makes them most appropriate for analyzing reservoirs and rigs.
Cognitive systems can analyze individual reservoir data to detect the quantity and quality of hydrocarbon present in that particular wellbore. Businesses also have to make sure that they understand changing political, commercial, and legal risks before investing in an acreage. Cognitive systems can understand the context of such risks and combine them with other unstructured data to arrive at conclusions. These conclusions and ability to analyze each reservoir data independently helps oil and gas organizations to make the most suitable decisions for land and reservoir acquisitions.
Streamlining Well Exploration
Once businesses buys an area for oil and gas extraction, they plan strategies to explore wells and extract minerals from the reservoir. The strategies include parameters like machinery that will be used to drill and timing to drill for optimal productivity, among others. Businesses can use AI-based robots for drilling and increased productivity. But, AI systems cannot assist in building exploration strategies. Also, sudden weather changes require strategy changes for optimal production. Cognitive computing can not only predict weather changes like AI but also identify possible strategy changes based on changing weather in real time. This helps in streamlining well exploration without any cutdown or halt due to strategic changes.
Cognitive computing can also extract information about reservoirs’ condition and quantity of minerals present in it. With such information, cognitive systems assist in improving existing machinery facilities or suggesting new ones. With such assistance, oil and gas organizations can enhance production and minimize mineral extraction shortfalls.
Determining Maintenance Frequency
One of the major reasons for production shortfall in the oil and gas industry is that the machinery used usually do not operate efficiently to their maximum potential. According to McKinsey, typical offshore platforms on average run at 77% of their maximum potential. And this is because of the lack of maintenance insights. Usually, a schedule is prepared for equipment maintenance services. But depending on the age and working conditions, different equipment requires different maintenance services. For instance, a machine that is being used for say 5 years requires more frequent maintenance services than the one that is used for only 1 year.
With IoT, cognitive systems can monitor each machinery used for well drilling and exploration. They can consider various factors like machinery age, conditions under which they operate, and frequency of their use to determine optimal maintenance frequency. This increases the lifespan of machines and their production potential. Thus cognitive systems also help to avoid any equipment downtime which can be very cost-effective to oil and gas businesses. Based on a study, offshore oil and gas operators can lose anywhere between $49 and $88 million annually due to unplanned downtime. Cognitive computing can, therefore, minimize downtime by determining maintenance service frequencies for individual machinery.
Detecting Optimum Drilling Time
Cognitive computing systems equipped with both historical and current well and environmental data can determine the best time to drill a wellbore. For instance, historical data can help cognitive systems understand the best time for employee productivity. And current data can help them understand changing weather patterns and strategies reflecting them to detect optimal drilling time for quick mineral extraction. For example, cognitive systems can predict the probability of rain. And then they can suggest strategy changes according to them so that the drilling is not interrupted. They can also understand the context of businesses and suggest tasks and processes that can be done when drilling is put to a halt because of weather conditions.
Developing Hired Talent
The oil and gas industry includes several labor-intensive tasks. These laborers at times have to move down into reservoirs for monitoring machines or transporting extracted minerals. And these tasks can be risky because of heavy load equipment and hazardous gases. Cognitive systems can help businesses to develop hired laborers' talent to avoid any casualties. With the help of eye-tracking technology, cognitive computing can detect how an employee performs in exploration wells. For instance, they can monitor where a laborer is looking while performing a specific task. Cognitive systems can then match the data with eye-movement data of historical incidents that caused casualties. If they find any match, then they can alert managers and HR personnel to train laborers on how to avoid such incidents.
Cognitive systems can individualize each laborer. They can then suggest training and learning paths for individual employees based on their needs and requirements. And cognitive systems can also help managers to promote or transfer an employee from one department to another based on their skills.
Finding all the above-mentioned applications among many others, cognitive computing in oil and gas can surely prove to be a game-changer. But to make it a complete game changer developers can combine it with other modern technologies like big data and IoT to explore new possibilities in the oil and gas industry. IoT in oil and gas can facilitate operation integrity by providing predictive maintenance, pipeline monitoring, and location intelligence. They can also enhance upstream and downstream operations by providing real-time machine and sensor integration, dashboards and trends analysis, and network of asset information. Developers can also converge cognitive computing with blockchain to secure it from cyber-attacks and data breaches.
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