Artificial Intelligence (AI) answers the question of how to increase production and assets without increasing operational workforce. With AI, every field operator becomes that much more equipped, less compartmentalized, and more connected in their ability to produce and make decisions. In the oil and gas fields, so much valuable operator time bleeds out on tasks because operators lack access to intelligent machines that could better route them to high-value production problems to solve. AI bends time for humans to do what they do best — make important decisions.
The technology is ready and the time is right for the oil and gas sector to embrace AI to empower their employees and supercharge their operations. Early adopters of AI will reap the benefits of this game-changing technology, forcing the rest of the industry to play catch up or be left behind. AI is already proving itself in a wide range of applications in the field.
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Codifying and Replicating More Skilled Lease Operator Behavior
With initial business logic fed through existing behaviors, AI self-learns functions that are mission critical and drive production excellence to help field operators make intelligent decisions and prioritize tasks. This enables companies to become less dependent on every operator knowing specific routes, and rapidly onboard their crew to know more routes, thereby making room for more “Super Lease Operators,” or floaters, without waiting for experience to catch up. With AI, more of the field workforce gets to focus on keeping wells online while finding innovative ways to maximize production.
Well Management
Well management is a complex and multifaceted process involving a wide variety of data. SCADA data, production accounting information, drilling and completion information, maintenance and reliability data, in addition to well header data, all combine to guide operators. When this scale of information is involved, nothing beats the processing and automating ability of AI. These intelligent systems aggregate all relevant information, allowing operators to make the right decisions without delay.
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Predictive Maintenance
Machinery slows down and breaks, then requires maintenance. Traditionally, this has just been seen as a cost of doing business. But with machine learning and AI, we now know machines can become more predictable in their maintenance needs, given the right data. Prediction in any sense requires data, and the more data that can be analyzed, the more accurate a prediction will be. The fact is that humans can only take in a certain amount of data, whereas AI can analyze a far greater amount, making more accurate predictions possible.
AI’s predictive maintenance not only allows machines to have scheduled jobs before they break, saving on costly downtime, it also cuts down on labor costs.
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Changing Conditions
Oil and gas is a dynamic business affected by several geopolitical, climate, and market factors out of the company’s control. Changing weather conditions can disrupt operations with only days’ or even hours’ notice, while new regulations can come into effect that negatively impact oil field processes. Workers need to be up to date on all these changes and know how to adapt in order to operate safely, in compliance, and without delay.
AI helps keep track of all these changes and ensures that all workers are provided with the information they need to stay up to date. Real-time alerts sent to mobile technology also offer another layer of protection, so whatever happens, AI keeps everyone in the know wherever they are.
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Automate Tasks in the Field
Manually created work orders are still common in the oil and gas industry, but they are not executed efficiently in today’s fast-paced oil field landscape. Manually entered and distributed work orders have an enterprise back office system but suffer from a lack of field communication feedback loops, extra costs for data entry employees, and frequent errors, which lead to reduced productivity.
By combining AI and mobile technology, work orders can be intelligently picked up by the most available or applicable field operator who is able to complete the assigned tasks. AI learns from field operators’ crowdsourced ratings, their skills levels, and several other production-related factors, thereby reducing the effort required to send and acknowledge right task-resource allocation. This empowers the field workforce to focus on more challenging tasks that elevate their job role and the firm’s bottom line.
Instead of making humans work like machines, it is time that we let the machines automate the obvious processes and allow humans to be humans — intuitive, collaborative problem solvers. In this data age, AI can really fuel the power held in oil field automation.
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