The Oil and Gas (O&G) industry, though seemingly one of the most lucrative to work with, has been battling several challenges in the last few years. Demand evolution, regulatory concerns, supply diversification, rapidly growing infrastructure and resulting frequent asset downtimes, siloed operations, and environmental, social, and governance (ESG) risks continue to plague the industry with difficult-to-conquer obstacles.
Particularly, as concerns about pushing forward a green, zero-waste agenda emerge rapidly, the oil and gas industry is under continual pressure to reduce carbon dioxide and other potent emissions.
Consequently, like every other sector, O&G is exploring the vast potential of Artificial Intelligence (AI) applications to increase productivity, boost security, enhance equipment availability, maintenance, and uptime, and enable sustainable operations.
Artificial intelligence has opened up a whole new spectrum of possibilities in the oil and gas value chain, enabling proactive and predictive asset management, boosting data-driven decision-making, building a connected employee base, and ensuring the health and safety of the workforce.
According to research, the global artificial intelligence in the O&G sector will be worth $3,669.8Mn by 2027, growing at a CAGR of 10.81% between 2022 and 2027.
This blog will go over six outstanding use cases of artificial intelligence in the oil and gas industry.
AI Applications in the Oil and Gas Industry
Darryl Williams, Corporate Vice President of Energy at Microsoft, rightly said, “Technologies like AI and machine learning can analyze the past, optimize the present, and predict the future.”
In light of this thought, let’s explore how AI/ML technologies are shaping up “digital champions” in the oil and gas space.
1. Smart asset management using Digital Twins
The asset-intensive nature of the oil and gas (O&G) industry demands continuous asset monitoring, management, and maintenance. According to research, 63% of oil field assets are past the halfway point of their expected lifetimes. As a result, equipment reliability is a significant issue warranting data-based asset management and maintenance.
In this regard, the concept of digital twins has brought about a revolutionary change in asset management and maintenance in the oil and gas industry.
What is a Digital Twin?
A digital twin is a replica of an actual on-field asset.
It replicates the attributes and features of a physical asset, such as pumps, compressors, turbines, and pipelines, enabling virtual monitoring of on-field assets. It leverages reliable, high-quality data to augment asset performance.
As the digital asset is updated with all current operational data, including schematics, operating information, maintenance history, and troubleshooting processes, a centralized repository of asset data is created. This data repository is analyzed by AI algorithms in real time. Machine learning algorithms identify variations from usual patterns to enable the following:
2. Driving workplace safety
Powerful technologies like AI, machine learning, IoT, and Big Data monitor on-field operations to identify fatal signs, such as hazardous gas levels and unauthorized personnel access.
AI-enabled chatbots then issue real-time alerts on mobiles and smart wearable devices, such as health or lockout emergency notifications, permitting them to work on-field, thus creating a more connected on-field workforce.
Additionally, smart watches, safety helmets, biometric vests, and bluetooth tags monitor workforce activities, track field operator location, identify signs of workforce fatigue, and enable access to critical information on the field.
IoT and AI can detect and troubleshoot on-site hazards in real-time or send notifications to dispatch experts as and when required.
3. Optimizing production and scheduling
Budget and schedule overruns are common problems that plague offshore oil projects. Factors such as weather delays, resource constraints, and scheduling risks play a key role here.
What makes the process more complex is the large number of siloed activities, such as drilling and platform installation, comprising the buildup period of oilfield development. In this context, it is vital to find robust project planning and scheduling models that take into consideration the interdependence of these interacting components and the risks involved therein.
Cloud-based platforms capitalize on powerful analytics and AI algorithms to study incoming data for anomalies, signaling signs of trouble in monitored equipment.
4. Analytics-based decision-making
Oil and gas firms generate reams of siloed data, including reservoir characterization, seismic and microseismic data, drilling time, performance and recovery, shipping and transportation data, and much more, produced by multiple business lines and processes across different geographies. However, these companies often lack the ability to use this data to arrive at meaningful insights and conclusions.
Employing data scientists to analyze this data is time and cost agnostic. AI-based applications help create a unified data platform with knowledge-mining capabilities to make information searchable and derive intelligent and meaningful insights and predictions from it.
AI, machine learning (ML), analytics, and industrial IoT were the top game-changing technologies in 2021.
By integrating, analyzing, and visualizing such diverse data (using ML, big data analytics, and mobile devices), oil and gas enterprises can achieve the following:
5. Smart inventory, procurement, and supply chain management
AI, machine learning, smart track-and-trace technologies, and cloud networks help the oil and gas industry augment enterprise resource planning (ERP) and optimize inventory, logistics, and warehouse management. They also enable smart procurement, shipment transparency, replenishment, and digital category management.
IoT-linked sensors and intelligent devices transmit fleet data such as vehicle performance, fuel consumption, and inventory to schedule maintenance and avoid equipment failure.
Low-code AI-based ERP solutions can digitize and automate the material master request (MMR) authorization process, reducing manual intervention, expediting document approval, eliminating paper-based material request approvals, and providing 100% accuracy and traceability in the MMR process.
6. Back-office process optimization
Oil and gas businesses have various back-office operations that can be automated to increase efficiency, save time, and reduce operational costs.
Oil and gas CIOs must adopt technologies to automate the following processes:
How can Acuvate help?
Acuvate uses robust smart, new-age technologies like AI, Big Data Analytics, IoT, and cloud computing to build enterprise apps and platforms that support intelligent analysis and collaboration, thus redefining information orchestration and operations in the oil and gas industry.
Our smart and sustainable digital solutions transform operations across the oil and gas value chain, modernizing asset management, building a connected value chain, ensuring health and safety at the workplace, and enhancing efficiency through data-driven decisions.
Connect with our experts to know more.