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Realizing the potential of AI in the oil and gas industry


Artificial Intelligence (AI) is easily one of the hottest areas of technology today, and for good reason— it holds promise in so many areas. Gartner predicts that by 2021 the dominant source of AI business will be new revenue, and by 2021 AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity. This means businesses looking at both efficiency and reducing costs should investigate the promise of AI.

AI is already making headlines in industries like retail, banking, hospitality and high tech, and this progress will speed up as other industry factors come in to play. Super-fast computing power with the ability to analyze large datasets, an IoT revolution driven by Internet connectivity, and the on-demand scalability of cloud infrastructure are opening possibilities for AI across many industries.

The oil and gas industry is beginning to experiment cautiously with AI-based solutions. As a whole, the industry is facing challenges with pervasive cheap oil prices, new reserves and alternate fuel choices. These have put pressure on profit margins, driving the industry to explore AI options.

Machine and deep learning can be applied to bring a quantum change in operational efficiency to reduce waste and non-productive time, and prolong asset life and performance. It is an opportunity the industry needs to seize and move forward.

Making better business decisions

Deep learning algorithms can be applied to train neural-network models to analyze vast amounts of data for decision making. Rendering complex multi-layer neural networks on terabytes of data is a reality today. Deep learning can also be used to develop more sophisticated neural networks to handle asset management in the oil and gas industry. For example, time-automated condition monitoring with predictive analytics can assist with operational decision making.

Similarly, unsupervised learning enabled by deep learning can be used to reveal patterns, while drones are being used for visual inspections of pipelines. As a drone flies over a pipeline, it records video footage that may contain signs of leaks or cracks that are undetectable to the human eye. Deep-learning algorithms can automatically detect pixel signatures from drone footage, minimizing asset failure risk at a much-reduced cost and in a timely manner.

Another emerging AI opportunity is the use of conversational bots that apply natural-language processing algorithms enabled by deep learning. These bots can be used by field workers to interact with asset diagnostic applications using speech to text and vice versa.

The AI possibilities are endless and the potential impacts in the oil and gas industry include:

AI capabilityAsset-management scenarios
Predictive asset maintenanceReal-time asset operations monitoringAsset inspectionAsset work order managementCondition-based monitoring
Computer vision: image recognition and classificationSupervised, unsupervised learning and deep learning to identify anomalous behavior and predict failure and corrective actionsRobotic applications: drones using deep learning algorithms like RNN/CNNAssess the asset condition by automated visual inspection using drones enabled with image recognition/deep learningDrones enabled with deep learning image recognition features can monitor assets in remote and hard-to-reach locations
ClassificationAnomaly detection on time-series data using supervised and unsupervised learning algorithmsIdentifying deviations from normal operations using anomaly detection and taking automated corrective actions
ClusteringUnsupervised clustering techniques can predict various possible asset health conditionsUnsupervised cluster analysis can throw light on various symptoms of process or asset degradation
RegressionFailure prediction using regression models
Natural language processing/ query generationReinforcement learning algorithms like recurrent neural networks can be used to implement and improve NLPUse of conversational bots as assistants to the maintenance workerWorkers can use bot assistants to log details about asset health and perform immediate diagnosis
Speech recognitionVoice-based communication to access information from knowledge databases and communicate with expertsThe sound from rotating assets can be analyzed for early maintenance issues
Voice to text/text to voiceBot assistants can help field workers process information in knowledge databases and even facilitate communication with experts
Pattern recognitionVarious parameter behaviors can be monitored for specific patterns using unsupervised learningRobots/drones can identify patterns using visual footage to detect leakages, corrosions, etc., early in assets
Association rule learningUnsupervised learning algorithms can be used to identify correlations and associations and help decisions in asset health monitoringEarly detection of deteriorating asset health using unsupervised learning algorithms

The promise of AI and transformation

The recent advancements in deep learning and machine learning have brought significant changes to predicting and determining attributes, including insights on anomalous behavior, digital signatures, changes and patterns.

It is true that AI has not always been the most effective solution, and this has led to misconception and hype. AI is still slow in learning new concepts and extending that learning to new applications. However, AI is still in its early stages of development for the oil and gas industry and one can extrapolate recent successes into the future.

For example, AI systems still need a tremendous amount of data to train themselves, while humans do not need to look at 50,000 cat images to identify a cat. In comparison, a human child can look at two cats and distinguish between a cat and a dog. So, in some areas, like speech recognition, AI is more sophisticated than the human mind, while in domains that require reasoning, contextual understanding, and goal seeking, AI cannot even compete with the intelligence of a child.

Even with limited development, companies such as Amazon are using AI to gain a competitive advantage in the market. Soon, every company will need to tap into the potential of AI.

Oil and gas companies must adopt AI in order to compete and grow in the market. We envision a future where AI capabilities will differentiate the winners from the laggards in the increasingly complex energy marketplace, and whoever adopts faster will gain the advantage.

Neeraj Kumar

Digital Solutions Lead, NA Energy Practice