In a time when the world is claiming that data is the new oil, it is woefully ironic that the oil industry is so far behind the tech curve when it comes to digitizing their processes and taking advantage of big, networked data.
Ranked by MIT Sloane Management Review and Deloitte as among the least digitally mature industries in the world, oil and gas companies have nothing but potential in front of them—potential for growth, boosted efficiency, flexibility in responding to dynamic trends, and better exploration and production. What exactly does digital transformation in the oil and gas industry entail, and in what areas can technology help bring the energy sector into the data-centric world of the 21st century?
What is Digital Transformation?
Digital technology can come in several forms—from communication tools to data sensors, from automated processes to analytics designed to inform decision making, and everything in-between.
These innovations have helped improve multiple markets and industries over the past decade. IBM is using AI and Machine Learning to improve agricultural and farming practices. Data analytics has proven useful in fine-tuning supply chains and logistics. Even brick and mortar retailers have started using passive sensors to build trends in customer demographic and habits within their stores in real time.
Looking forward, the Industrial Internet of Things is projected to add $15 trillion in value to the global economy by 2030 through networking every machine, providing data to every variable, and enabling remote, automated assistance to every process for an industry.
What Does This Mean for the Oil and Gas Industry?
As more and more industries move forward with integrating digital technology into their business practices and decision making, the oil and gas industry has lagged behind. This is primarily due to its sheer size, complexity, and maturity. A century ago, when computers were barely reaching the minds of science fiction, oil was already one of the biggest and most expansive industrial endeavors in human history. Overhauling methods and processes that are often worldwide in scope and utilize billions of dollars in machinery and hardware each day is not something that can be done overnight.
Courtesy of the 2008 recession, the spiraling oil costs that followed it, and changing global trends in the energy sector, companies have found themselves scrambling to boost their bottom line. The solution may be digitization through partnering with companies with cloud computing and AI capabilities. This helps the oil companies to make decisions informed by data analytics that increase efficiency while driving down cost—decisions such as when to conduct maintenance on equipment to maximize service life, what processes can be sped up or automated, and identifying best practices to standardize and replicate.
What was once an excuse for avoiding overhaul (size, cost, and complexity) is quickly becoming a critical reason why a company needs to go digital. When processes become too vast and complex for humans to manage, digital platforms and data analytics tools consolidate these unwieldy industries and shrink them down to accessible, real-time information. Decision makers can then use this data to not only react quickly to their organization’s needs, but to even predict future trends and anticipate future issues—solving them before they get out of hand.
Implementing Digital Solutions
Oil and gas companies save $50 million on average when they switch to digital processes, according to Baker Hughes. There is no question that digitization can increase the value of a company’s assets in every stage of the production curve. Some areas where digital platforms can be implemented include:
- Marketing Optimization. Artificial intelligence and data analytics is already leading the charge in revolutionizing marketing campaigns. Digital technology can help an oil and gas company create targeted, effective marketing that both gets results and fine tunes itself to be constantly evolving and improving.
- Research and Development. Data analytics and environmental sensors can provide insight that helps improve exploration into new areas and resources. This is similar to how cloud computing and sensors are being used to measure soil content, sunlight, moisture, and other environmental factors to help farmers fine-tune their crops for maximum yield.
- Operations Management. Artificial intelligence can predict operational events. Historical data can help identify the most efficient operations practices as well as processes that need to be changed or improved. The operations field is where data analysis can really provide executives with the information they need to understand the health of their organization and make impactful decisions.
- Procurement Cost Comparisons for Products and Services. Data repositories of external vendor and supplier data can quickly identify the best partner to purchase from based on cost and availability,
- Collaboration Tools for Large Teams. Many oil companies employ thousands of members stretched across the globe. With such complex teams, collaboration and communication can easily suffer. Intuitive communication channels that allow for real time collaboration are a must, and digital services can provide seamless solutions that allow a team to integrate quickly and efficiently, regardless of size or location.
Conclusion
As the world constantly moves forward, it is important not to get left behind. Companies looking for progress must follow the age-old maxim of change or die in order to find success—and that means embracing digital transformation.
This is where SPRI can help. With our team’s cumulative 265 years of oil and gas experience, we can help the upstream oil and gas industry apply cost-effective digital innovations that can help them thrive in today’s world. Contact us today to learn how we can help you.
Sierra Pine Resources International works with clients throughout the upstream oil and gas industry to solve problems and provide solutions that increase the value of their assets in every stage of the production curve.
