Modern emerging technology is shaking up traditional ways in which the oil and gas industry operate, with automation software and data-driven solutions set to transform the field. By moving toward work automation through information technology, the oil and gas industry faces new changes and challenges.

Many oil and gas companies haven't yet adopted digital innovations to improve important performance indicators. According to McKinsey research, even though almost every company has tried digitization in various parts of its operations, 70% have only tested digitalization solutions and not moved beyond that initial phase.

As the focus switches from volume growth to efficiency and profitability, oil and gas companies will adapt more technology advancements, not only in the field, but also to streamline operations and processes. A new digital workforce is also entering the job market, demanding more modern solutions.

The main obstacles to adopting emerging technology in the oil and gas industry are not the technologies themselves. It's the cultural and organizational barriers. Overcoming these hurdles is crucial for success in the future of work automation in this sector.

Resistance to Automation of Business Processes

Where are the documents stored? How is the efficiency of operations monitored?

Navigating the automation landscape in the oil and gas industry presents several challenges. These include:

1. Skills shortage: Long-standing STEM shortages limit the availability of specialized resources in the industry.
2. Job displacement concerns: Automation software focusing on laborious and repetitive tasks raises concerns about potential job displacement, especially in administrative roles.
3. Cost and business model changes: Introducing new technologies for the supply chain can be expensive. Significant regulatory changes affecting how a business operates may also be needed.
4. Human judgment: Human judgment is a valuable skill not easily replicated by technology. There's a need for human involvement in maintaining and ensuring the efficient operation of automated systems.

Handling data and documents in the oil and gas sector can be complicated because there are strict regulations and large amounts of data to handle. Organizations must prioritize effective records management to ensure proper maintenance, prompt availability, and appropriate disposal of necessary records. Are there concerns about technology integrations with multi-faceted, legacy systems?

Could this technology investment worth the effort?

Work Automation's Digital Transformation

Artificial intelligence (AI) and machine learning transform data analysis and processing. This advanced technology offers predictive know-how to enhance decision automation in exploration, production, and distribution processes.

The Industrial Internet of Things (IIoT) is expanding, forming a connected system. This system enables more effective, real-time monitoring of equipment and facilities. It helps monitor machines and buildings in real-time, making things run smoothly and stopping problems before they worsen. The result is an improvement in operational efficiency and assistance in preventive maintenance measures.

Blockchain technology is also emerging as an oil and gas supply chain disruptor. Its secure and decentralized setup makes things clear when tracking transactions and ensuring they're real. Blockchain technology helps fight fraud and reduces information chaos. It establishes a trustworthy and immutable record, fostering a more organized and reliable supply chain ecosystem.

Lastly, the adoption of drones in asset inspection is transforming the future of work. Drones provide a cost-effective and efficient means of surveying vast and remote oil and gas facilities. They reduce the need for manual inspections and improve safety protocols.

Automation's Impact on Traditional Workflow Structures

From discovering to refining oil, work automation leads to improvements such as:

• Streamlined operations
• Less human effort
• Optimized workflows

AI-powered autonomous drilling improves precision and speed in the upstream sector, increasing productivity. An example is real-time identification of rock types and potential failures while drilling with machine-learning algorithms. The algorithms can save up to 20% of time and up to 15% of costs in well construction, maximizing the connection between the wellbore and the pay zone.

Work automation also helps with transportation, making things more reliable and safer in midstream and downstream. Automated control systems monitor things closely, reduce accidents, and strengthen operations.

Even though work automation is helpful, the profession still needs people with specific skills. Learning new skills is vital to keep up with these changes. The world becomes more digitally connected as the oil and gas industry increasingly uses workflow automation.

AI Automation in Real-World Scenarios

Advanced technologies address concerns and limitations, ushering in a new era of possibilities. AI and automation enhance economic efficiency and play a crucial role in reducing costs and improving financial performance. Being quick to embrace modern technology gives companies an advantage because they understand its importance. Success stories further reinforce the positive outcomes of business process automation solutions.

Here are a few examples that illustrate oil and gas companies using AI for knowledge management:

• BP uses AI to improve drilling and reservoir modeling. They've created a smart computer program that examines seismic and geologic data to determine the best drill places. BP also uses AI to predict when equipment might need maintenance, reducing downtime and increasing safety.
• Chevron uses AI to optimize operations and reduce energy usage. Their innovative process automation system predicts how well their refineries will perform and finds ways to use energy better. Chevron also uses AI to make drilling better and safer.
• ADNOC invests in AI to streamline operations, especially by analyzing real-time drilling data. Their smart drilling system adjusts in real-time for improved accuracy and better workflow.
• Shell uses AI to make things better by finding and refining oil. Their AI system predicts when equipment might break so they can fix it before it causes problems. Shell also uses AI to improve their supply chain and retail operations.

The Future of Automation

The move toward work automation makes the oil and gas industry more efficient and technologically advanced. This improvement also helps the industry tackle challenges across the global energy landscape. Advancements like data analytics and predictive maintenance algorithms can optimize asset performance.

These new developments reduce downtime and enhance the overall reliability of oil and gas infrastructure. Green energy integration using AI and work automation may also help meet environmental standards. In addition, implementing blockchain in supply chains can enhance transparency, tracking, and security.

This innovative approach streamlines logistics, reduces fraud, and ensures accountability across the entire oil and gas value chain. These changes suggest future automation for the industry marked by efficiency, sustainability, and technological advancements. There’s also great potential for this to reshape the oil and gas industry's entire role in the energy system.

FAQs

What benefits do knowledge workers gain from automation in the oil and gas industry?
Knowledge workers stand to benefit from enhanced economic efficiency, reduced costs, improved financial performance, and streamlined operations enabled by work automation. Automation of knowledge work also plays a crucial role in ushering in new possibilities and addressing industry concerns.

What are the disadvantages of automating knowledge work in the oil and gas sector?
While automation brings numerous advantages, there are still challenges to consider. Potential disadvantages include:

• Cultural and organizational barriers
• Skills gap and shortages
• Job displacement concerns
• The need for significant changes to business models

These challenges highlight the importance of carefully navigating information automation.