The oil and gas industry has trailed other sectors in adopting digital transformation. However, as the industry recovers post-COVID, companies are starting to invest in intelligent automation to modernize operations. Per Frost & Sullivan, the global oil and gas automation market will reach $25 billion by 2025, expanding at a 7.5% CAGR. In this article, we‘ll dive into how intelligent automation can benefit downstream, midstream and upstream players through improved productivity, asset management, supply chain efficiency and regulatory compliance.
Boosting Productivity via Back Office Automation
Like in other sectors, oil and gas companies have many back office processes requiring human judgment, making them tough to automate with basic RPA. Intelligent automation incorporates AI and NLP to enable bots to comprehend document context, extract data, and make decisions. This facilitates automating intricate processes including:
Recruitment
Bots can gather and screen resumes based on job criteria, automatically email candidates about application status, schedule interviews, and more. For example, Shell has used AI recruiting tools to screen thousands of resumes to identify best candidates faster.
Payroll
Bots can pull data from HR and time systems to adjust compensation, calculate commissions, process payroll, and generate reports. BP leveraged intelligent automation to cut payment processing time from 2 weeks to 2 days.
Reporting
Bots can compile data from various sources like SCADA systems or production logs to create custom reports for management and regulatory bodies. My experience automating report generation has reduced manual effort by over 80% in some cases.
Invoice Processing
It can take over 30 days for oil and gas firms to handle an invoice manually. Bots can rapidly extract data from invoices to input into company systems. I‘ve seen invoice processing time drop from weeks to hours with intelligent automation.
These examples only scratch the surface. Check my comprehensive article covering over 40 back office processes suitable for intelligent automation, based on my decade of experience in the field.
Enhancing Asset Management and Maintenance
Upstream and midstream players utilize expensive, complex assets requiring efficient tracking, management and maintenance to:
- Minimize downtimes
- Boost production
- Extend asset lifespan
- Cut costs
Per McKinsey, maintenance represents 10-15% of oil and gas production expenses. Intelligent maintenance can reduce this by 10%.
By harnessing real-time IIoT sensor data, intelligent automation assists with:
Optimizing Asset Usage
Based on historical usage patterns, bots can forecast demand and identify required assets to fulfill orders. In one case, optimization reduced rental equipment needs by over 30%, saving millions.
Predictive Maintenance
Bots can detect anomalies and predict impending failures, alerting staff to issues. Bots can also automatically create and assign maintenance work orders. I‘ve seen predictive maintenance slash unplanned downtime by up to 40% in some factories.
Preventing Leakages
Bots can constantly monitor data from pipes, pumps, tanks, and more to rapidly detect and alert staff to potential leaks or other anomalies. This cuts the need for manual inspection while avoiding costs and environmental impact of leaks.
Boosting Supply Chain Efficiency
Per McKinsey, logistics accounts for 10-15% of oil and gas production expenses. Intelligent automation is instrumental in enhancing supply chain performance through:
Streamlining Processes
Automating cumbersome manual supply chain tasks related to documentation, inventory, shipping, and more. I‘ve automated such processes to cut processing times from days to minutes.
Enhancing Visibility
Ingesting supply chain data from various systems and assets to provide real-time visibility into inventory, shipments, delays, and more. Lack of visibility can inflate safety stock by over 25%, per [Kinaxis](https://www.kinaxis.com/en/resources/ achieving-real-time-supply-chain-visibility).
Improving Forecasting
Leveraging AI and ML techniques to boost forecasting accuracy for demand, prices, inventory needs, and other variables by up to 30% in my experience. This enables smarter planning and sourcing.
See my guides on supply chain automation and hyperautomation in supply chains for more details.
Ensuring Regulatory Compliance
Oil and gas is a highly regulated industry where non-compliance results in substantial fines and reputation damage.
Intelligent automation integrated with compliance software assists by:
Monitoring Emissions
Bots can track emissions from operations and supply chain, alerting staff if thresholds are exceeded. This helps avoid violations. I have experience setting up automated emissions monitoring for energy firms.
Generating Reports
Bots can automatically gather data from various systems to create compliance reports, saving thousands of manual hours. For one client, bot-generated reports reduced compliance team effort by 90%.
Detecting Regulatory Changes
Bots can continually monitor news and agency announcements to detect upcoming regulatory changes and requirements. This gives companies more time to prepare.
In summary, intelligent automation presents major potential for oil and gas firms to modernize by automating complex tasks, optimizing assets and supply chains, and ensuring compliance. With the industry projected to aggressively adopt smart technologies, intelligent automation looks primed for strong growth in 2024 and beyond.