Machine Learning (ML), a type of artificial intelligence (AI), has been shown to be beneficial in many industries. ML systems can automatically learn and improve from experience without being explicitly programmed. They do this by accessing data and learning from it. This makes ML a powerful tool for the oil and gas industry, where there is a vast amount of data available.
Implementation of Machine Learning
Exploration
Analyzing seismic data is a complex task that requires a deep understanding of geology and seismic interpretation. ML can help to automate this process and accurately identify potential oil and gas reservoirs.
Drilling
ML can be used to predict future production from a well through predictive analytics. This information can help drillers make decisions about where to drill next and how to optimize production from existing wells. It can also be used to identify the optimal drilling paths. This can help to reduce the risk of drilling into unexpected formations and improve the chances of finding oil and gas.
Maintenance
ML can be used to predict when an equipment is likely to fail. This allows for preventive maintenance to be performed before a failure occurs, which can save money on repairs and downtime.
ML can help to optimize maintenance by recommending tasks based on the equipment’s condition and usage history. This can help to ensure that maintenance is performed when it is needed, and that it is performed correctly.
Safety.
ML can change the safety protocols in the Oil and Gas industry in a revolutionary way. Images captured by drone or other surveillance devices can be analyzed using ML engines to detect potential safety hazards like leaks, structural damage, or equipment malfunction.
NLP (Natural Language Processing) a part of ML can be used to analyze tonnes of safety reports, mostly maintained as a hardcopy in a traditional industry like Oil and Gas, to analyze patterns in occurrences of certain repetitive failures and identify the root cause.
Use cases of Machine Learning in Oil and Gas Industry
Schlumberger
Schlumberger is using ML to improve the efficiency of its drilling operations. The company has developed a system that uses ML to analyze real-time data from drilling operations and identify potential problems. This system has helped Schlumberger to reduce the number of drilling failures and improve the overall efficiency of its drilling operations.
Halliburton
Halliburton is using ML to improve the performance of its oil and gas wells. The company has developed a system that uses ML to analyze production data from wells and identify opportunities to improve production. This system has helped Halliburton to increase the production from its wells by an average of 10%.
Baker Hughes
Baker Hughes is using ML to improve the safety of its oil and gas operations. The company has developed a system that uses ML to monitor real-time data from oil and gas facilities and identify potential hazards. This system has helped Baker Hughes to prevent accidents and improve the overall safety of its operations.
Challenges to ML Implementation in Oil and Gas
Machine Learning is already being implemented in the Oil and Gas industry as a driver of change. However, this transformation is not something that can be achieved instantly as several hurdles are strewn in the path of ML adoption.
Lack of trained professionals
The first significant challenge is the oil and gas industry’s need to hire data scientists qualified to extract knowledge from industry data. This may be difficult for companies that already have a shortage of workers or companies in regions where there aren’t many people with data science skills.
Provisioning IT Infrastructure
The second challenge is to build or lease a powerful IT infrastructure suitable for supporting machine learning processes. One important requirement is certainly having a cloud infrastructure to smoothly handle such large volumes of data. This may demand a migration from a traditional database.
Migration will require re-engineering or retiring existing IT infrastructure including the architecture of various applications in use into microservices, buying or leasing new software and hardware.
Cost Optimization
All these transformations will involve certain costs in terms of hiring an expert data science team or buying new software and cloud spaces for data analysis and modeling. It is important to accurately assess whether to have everything in house or outsource the IT team.
Conclusion
The above points indicate that it is inevitable to implement machine learning for oil and gas business owners to stay competitive. However, if the path to ML adoption seems daunting business owners may think of outsourcing the IT load to a managed team of IT professionals at a fraction of the in-house cost and continue to focus on core business activities.
