A recent IBM study involving more than 100 corporate-level oil and gas industry executives in 28 countries revealed that they expect to strategically partner with other organizations for R&D more than twice as much in the year 2030 as they do today. They also expect to conduct 38% less research in-house and 44% less through outsourcing.

Shifts to more challenging frontiers mean future R&D will be too complex and costly for any one company to manage on its own. Forward-thinking companies are increasingly seeking to partner with outside entities.

In December 2010, Gazprom Neft-NTTS signed an agreement with Russian State Gubkin Oil and Gas University and IBM to expand its existing collaborative efforts. The agreement is focused on implementing concepts and IT solutions that will help Gazprom Neft develop and operate oil and gas fields more efficiently.

This reflects a growing recognition that technology is a key “change agent” in today’s oil and gas fields. Energy demand is expected to increase considerably over the next two decades, driven primarily by demand from emerging countries with burgeoning economic power. Although governments and companies are investigating alternative sources to fossil fuels, the biggest challenge over the next 20 years will be figuring out how to extract more oil and gas from existing sources and discover new sources by better harnessing technology.

As part of the Gazprom Neft agreement, specialized IT solutions will be developed to advance the digital oilfield concept by enabling integrated intelligent management of oil and gas field development and operations. The solutions also will result in the creation of a centralized storage and processing environment for geological, geophysical, and field data as well as a knowledge management system.

High-performance collaboration

The first project will be to build a high-performance collaboration environment for approximately 200 geologists. The new environment will enable geologists to work together on building hydrodynamic 3-D/4-D models and efficiently interpret seismic study data using cloud computing technologies, distributed resource access, and model calculation and interpretation. Support for this resource-intensive, high-performance data processing will come from IBM supercomputers at Gubkin Oil and Gas University.

By integrating seismic and geologic data from multiple sources and using advanced data modeling combined with supercomputing (including seismic cloud computing or above-petascale resources), companies can find very remote reservoirs. The large Tupi field 180 miles (288 km) off the coast of Brazil is a prime example – results from drilling an exploratory well confirm that this discovery could increase Brazil’s current proven reserves nearly six-fold, but the oil is underneath 7,000 ft (2,135 m) of water, 10,000 ft (3,050 m) of sand and rock, and 6,600 ft (2,013 m, or more than a mile) of salt.

In addition to the collaboration environment, the industry-academia partnership embodied by the Gazprom Neft agreement will yield solutions for a unified field data storage and processing space and a system for real-time monitoring, optimization, and forecasting of field and individual well behavior.

To optimize reservoirs that already have been identified, a variety of enhanced oil recovery techniques have been developed. However, each technique adds more physical variables to manage and more volumes to estimate and track. By using advanced visualization to render larger amounts of complex data in more intuitive ways, companies can achieve improved decision-making and faster time to oil. And by drawing on spatial and temporal data assimilation from time-lapse seismic systems, companies like Gazprom can run predictive assumptions that dramatically increase efficiency in extracting oil and gas.

All of these approaches are very data-intensive. In fact, a single oil field can generate up to one terabyte of data daily (the equivalent of 109 movies on DVD). Fortunately, advances continue to be made in data analytics and processing. The Watson computer, recently seen on the game show “Jeopardy!” taking on two of the quiz show’s top-winning players, represents a major step forward in deep analytics and system design that can be applied to the oil and gas industry. Watson – the result of 100 years of computing at IBM – can collect, process, and understand data based on natural language within a matter of seconds, offering real potential for improving exploration, production, asset management, and maintenance.

The Gazprom Neft solutions will be based on IBM’s Chemical & Petroleum Industry Information Framework, which provides real-time integration across multiple disparate systems using industry standards. The framework enables integrated operations with a reference semantic model based on industry standards, a rules engine, and a visualization model. Completing this technical validation means the solutions will meet criteria for integration with the framework’s production operations domain; IBM software; and industry standards such as OPC, ANSI/ISA-88/ANSI/ISA-95, ISO15926, and IEEE 61970/68. The framework also will give Gazprom Neft real-time visibility into production, equipment, and performance information across its assets. This will help leverage the company’s investment in existing applications and platforms, maintaining a best-of-breed environment via the framework versus point-to-point interfaces.

Increased automation and collaboration will address the looming skills gap.

Freeing employees for high-skill tasks

Human capital is another critical challenge in the oil and gas industry. The same IBM study yielded a surprising finding in this area – namely, that concern about workforce skills availability is decreasing. Yet other studies show that executives are very concerned about the ability to recruit and retain skilled employees and that the oil and gas industry will face growing demand for technologists, strategists, scientists, and multi-energy and risk-management experts – an order that will be hard to fill because other industries simultaneously compete for these same skillsets as the oil and gas industry moves closer to the “Great Crew Change.”

The increased automation and collaboration made possible by digital oil fields can help address this looming skills gap. It enables more effective data collection, monitoring, communications, and knowledge and information sharing, which enables less-experienced employees to benefit from the expertise of seasoned veterans and frees the time for all skilled employees to focus more of their efforts on collaboration, innovation, and continuous process improvement.

No alternative but to get smarter

The odds of finding new sources of oil and gas are becoming more challenging, and the need to optimize both the upstream and downstream is becoming more pressing. In response, executives who watch the bottom line at major oil and gas companies worldwide will insist on more visibility into the financial impact of operating decisions. The harder it becomes to find oil and gas reserves and the costlier it becomes to offset risk, the more it makes sense to search for new reserves by collaborating with outside partners and turning to the latest technologies .