Big Data in Oil & Gas

Big Data and analytics are not new to the oil and gas industry. It has long dealt with large quantities of data to make technical decisions. Big Oil has dealt with Big Data from exploration and production equipment for decades. But adoption and rollout are still stuck in pockets, putting profits, the planet, and people at risk. In their quest to learn what lies below the surface and how to bring it out, energy companies have, for many years, invested in seismic software, visualization tools and other digital technologies.

Whether oil prices are soaring or sliding, analytics & digital technology plays a key role in finding more oil, extracting it efficiently, and keeping producers in the black. But adoption and implementation of new technologies can take decades, hurting financial, environmental, and human safety goals. As IoT expands the technologies and data available at every stage of the exploration and production process, and Big Data and analytics learn to make sense of it, companies must prioritize use cases that matter most. With mountains of structured and unstructured data, technology developers need to approach Big Data analysis problem first, identifying high-value use cases with measurable paybacks.

Big Data in Oil & Gas Is About More Than Just “Lots of Data”

Not all data is Big Data, of course, and not all analytics require the horsepower and organizational model that Big Data applications typically require. Still, advanced analytics can play an important role in improving productivity in unconventionals, conventionals and midstream operations in oil and gas.

The oil industry recognizes that great power and imminent breakthroughs can be found in this data by using it in smarter, faster ways. However, resistance regarding workflows and analysis approaches remains in place, as it has for the last 30 years. How does the industry bridge the vocabulary and cultural gap between data scientists and technical petroleum professionals? Ideas, applications and solutions generated outside the oil and gas industry rarely find their way inside. Other industries seem to have bridged this gap, but in talking to experts in the broader technology industry, the oil industry is seen as a “no man’s land” for new-age entrepreneurs, while major technology providers spend billions trying to enter it (e.g., GE, IBM and Microsoft).

Breaking into the oil and gas industry is difficult for analysts, but the need and potential for reward are great. Majority of the top 10 organizations in Fortune’s Global 500 are oil and gas companies. More than 20,000 companies are associated with the oil business, and almost all of them need data analytics and integrated technology throughout the oil and gas lifecycle.

Throughout last decades, the oil and gas industry focused on data integration, i.e., How do we get all the data in one place and make it available to the geo-scientists and engineers working to find and produce hydrocarbons? Since the turn of the century, technology development has mainly focused on software that integrates across the major disciplines to speed up old workflows. The industry has had many amazing technical professionals, but the idea of a “data scientist” is new, and should be considered alongside the petrophysical, geophysical and engineering scientists. The next decade must focus on ways to use of all of the data the industry generates to automate simple decisions and guide harder ones, ultimately reducing the risk and resulting in finding and producing more oil and gas with less environmental impact.

Landscape

Existing model-based predictive algorithms work well with historical time series data, but not so well with Big Data. Cloud technology certainly becomes beneficial in handling the volumes and variability of Big Data. But to an even larger degree, advanced predictive methods with self-learning capabilities to use new and previously unknown or unavailable data will truly be the breakthrough technology that provides better insight into process and asset dynamics.

With plunging oil prices, large oil and gas companies are using Big Data to manage risks, cut costs, and increase revenues. In contrast to information-based industries like telecom, oil and gas needs a targeted approach to Big Data. High-priority use cases fall into six main categories: boosting production rates, reducing nonproductive time, predicting equipment failure, decision support for project planning and trading, de-risking exploration, and regulatory compliance and early event detection.

Big Data is poised for a breakthrough in oil and gas because the technology for acquiring, analyzing and acting on it is coming together. Companies like Glori Energy and Environmental BioTechnologies are complementing seismic data with novel sensors while others like Silixa and HiFi Engineering are bringing hardware like connected tools and smart pumps.

Several technology developers are tapping Big Data to help oil producers manage health, safety and environmental impacts. NuPhysicia, for example, offers telemedicine for workers far from health care centers.

Oil and gas companies can exploit Big Data by identifying high-value use cases like reducing operational costs by anticipating bit-wear, optimizing rig utilization, and improving recovery factors. As growth slows, oil majors need to reduce risk in exploration and production, and the rapidly increasing volume of data collected on the oilfield provides an opportunity to do so, if companies can find the right targets.

Technically Complex, High Risk

Despite its astronomical revenues, the profit margin of the oil and gas majors is 8 percent to 9 percent. Finding and developing oil and gas while reducing the safety risk and environmental impact is difficult. The layers of hydrocarbon-bearing rock are deep below the Earth’s surface, with much of the world’s hydrocarbons locked in hard-to-reach places, such as in deep water or areas with difficult geopolitics.

The oil and gas industry has to manage risks on many fronts, and the assets employed are expensive and capital intensive. Any systems supporting the industry must be highly reliable, responsive and secure. Add to this equation the fact that many of the assets are geographically widely dispersed.

Oil is not found in big, cavernous pools in the ground. It resides in layers of rock, stored in the tiny pores between the grains of rock. Much of the rock containing oil is tighter than the surface on which your computer currently sits. Further, oil is found in areas that have structurally “trapped” the oil and gas – there is no way out. Without a structural trap, oil and gas commonly “migrates” throughout the rock, resulting in lower pressures and uneconomic deposits. All of the geological components play an important role; in drilling wells, all components are technically challenging.

Following are three big oil industry problems that consume money and produce data:

1. Finding Oil  Reservoirs are generally 5,000 to 35,000 feet below the Earth’s surface. Low-resolution imaging and expensive well logs (after the wells are drilled) are the only options for finding and describing the reservoirs. Rock is complex for fluids to move through to the wellbore, and the fluids themselves are complex and have many different physical properties.

2. Expensive production The large amount science, machinery and manpower required to produce a barrel of oil must be done profitably, taking into account cost, quantity and market availability.

3. Environmental and human safety concerns  Finding and producing oil involves many specialized scientific domains (i.e., geophysics, geology and engineering), each solving important parts of the equation. When combined, these components describe a localized system containing hydrocarbons. Each localized system (reservoir) has a unique recipe for getting the most out of the ground profitably and safely.

So finally ?

The oil and gas industry was, at one time, at the forefront of Digital Transformation in industry. Nearly 50 years ago, it was the first industry to use digital distributed control systems (DCS) to control refineries and other downstream plants. Then, with the deployment of the digital oil field concept, it became a leader in adopting digital representations of seismic data representing deposits and reserves. The challenge for the industry now is to implement the next wave of Digital Transformation and radically shift the way it approaches Asset Performance Management (APM).

While we have plenty of data and challenges to undertake, how do we bridge the gap between the two? One way the oil industry tries is through venture capital funds. Both Shell and Chevron have their own such funds to create an outlet to explore new ideas. While not enough, the majors have begun to embrace analytics and others are finding ways to follow.

The oil and gas industry need more cross-fertilization. As oil and gas companies awake to the potential of analytics, many jobs will be created for data scientists, opening a portal for new applications and ideas to enter the industry.

Many technology providers exist in the industry. The successful ones from the past decade must embrace big data analytics to succeed in the future. This is challenging enough, but it will also require a mindset change. Few are poised to do so, but such companies will have the strategic data and intelligence to train new applications to be smarter and provide more complete solutions than stranded static data in empty point software products.

The oil and gas industry has an opportunity to capitalize on “big data” analytics solutions. Now the oil and gas industry must educate “big data” on the types of data the industry captures in order to utilize the existing data in faster, smarter ways that focus on helping find and produce more hydrocarbons, at lower costs in economically sound and environmentally friendly ways.

Sources: #GE #Bain & Company #BCG #Big4 #ESDS #Analytics #McKinsey & Co