THE DIMENSIONS OF
ENTERPRISE INTELLIGENCE
Business Imperative
As businesses struggle to recover from a troubled economy, business innovators are renewing efforts to more effectively find meaning and insight in the masses of data they collect on a daily basis. With growth opportunities limited by weak demand, governmental interference and regulatory uncertainty, they look to continually identify cost savings and operate more efficiently. They are also focused on sustainability and safety, and on improving the productivity of their employees. By accomplishing these goals, these companies are positioning themselves to survive during the down economy and capture large profit margins when the economy turns around. We feel that one important contribution to gaining insight is to implement a holistic approach to gathering, analyzing and visualizing data that we call Enterprise Intelligence (EI). We define EI as a framework for bringing together data from a number of functional domains for analysis and display in the context of the decision makers’ needs.
Our experience with applying EI is that this information for upstream oil and gas customers includes the domains of finance, process, assets, and people. The data that represents these domains is normally acquired and stored in a diverse set of applications and databases. In fact, a particular business entity might have different parts of its complete data set in multiple systems, depending on the context of the user. These distinct systems generally are aligned with their unique users and business processes. For example, a production facility on an offshore platform can be viewed as a construction project with timelines and costs, a physical asset with drawings and specifications, a risk with likelihoods and outcomes, or as an operating expense, depending on whether the decision maker is a manager, engineer or a financial analyst.
Integrated information is also required to enable changes in the workforce. The demographics of the industrial workforce are changing as the senior “boomer” employees retire and a reduced number of younger, less-experienced employees enter the workforce. The younger employees have developed proficiency using social networking and collaboration skills that involve communicating with and using information from many different areas. We are finding that young employees are given broader and deeper responsibility than the previous generation because of the need to do more with a smaller, and more diverse workforce. This change dictates the need to provide a much more integrated view of enterprise data, since the employees need to consider multiple types of information to make optimized decisions. This information must be made more easily accessible, since younger employees who grew up with Google and Wikipedia need and expect quality, timely data to be available to them with little effort. As more organizations adopt the concept of Integrated Operations (IO) or Operating Excellence (OE) initiatives, this drives the need for broader access to information, common data vocabularies, and data models shared across disciplines and roles.
The IO concept has been used in the military for many years as a way of ensuring that various combat organizations know what other organizations are doing in the “fog of war,” which is inherent in a dangerous combat zone and prevents friendly fire incidents. They also find that they can more effectively adapt to changes in battlefield conditions and enemy activities. The concept was adopted by the process industry as a way of more efficiently managing complex process facilities, where it is vital that ongoing maintenance and quality testing activities be tightly integrated with the manufacturing activities on a 24x7 basis. This also ensured that procedures for emergencies were prepared and available at a moment’s notice during a crisis. This drove the development over time of internationally accepted standards that define common data models, ontologies and templates that are used to integrate all information sources and flows. This ISO standard in the process industry is known as ISO 15926.
The Norwegian Oil Industry Association (OLF) has adapted the ISO 15926 standard to define drilling and production activities for upstream oil and gas. It has recently been integrated with the drilling and production standards promoted by Energistics known as Wellsite Information Transfer Standard Markup Language (WITSML)
3 and the Production Markup Language (PRODML). There is now an emerging standard, called RESQML, for reporting earth model and reservoir data. These standards are the basis of the information sharing required to accomplish the goals of integrated operations. Another element of IO that is becoming more widespread is the use of real-time, two-way videoconferencing between onshore collaboration centers and offshore work locations. This permits senior managers and engineers to engage in collaborative processes for more than one remote work location, while maintaining ready access to information systems located on shore. This constant surveillance of offshore workers is not always welcome; one company reported that when it sent workers to the offshore rig to install the videoconference equipment, it discovered that the equipment had been thrown overboard.
In support of the development of several enormous offshore gas fields in western and southeastern Australia that is now under way by Chevron, INPEX Corp. and Santos, these companies are designing and constructing what are being called Operations Information Centers (OICs) for the ““Intelligent Oil Field.” These new facilities will employ an integrated view of all information for a collaborative team that will oversee and optimize the operations of the field drilling, production, pipelines, liquefied natural gas (LNG) plants and the shipping ports where the LNG tankers are loaded. These centers receive and consume a tremendous amount of real-time and historical information, including production volumes, process plant status and efficiency, energy use, environment and safety, and personnel. The technical requirements for data management, integration, and visualization for such facilities are very complex, and require a broader view of what “intelligence”means.
The Classical View of Business Intelligence
The classical view of business intelligence is a capability to provide financial rollup reports and the ability to drill down on data to better understand the trends and hidden facts. Our long expertise in managing, interpreting and analyzing business data leads us to take a wider view of the data needed to make good business decisions. Financial reports are fine for looking back in time to see what occurred, but they have limited value in forecasting or making real-time decisions. By analyzing the types of information available to business leaders and engineers, we have grouped the intelligence into four primary categories as previously discussed. These “dimensions of enterprise intelligence” are not tied to specific data types or information systems. Nor are they necessarily tied to specific workflows. They represent a way of thinking about how information can be used strategically in making better decisions to improve the efficiency and safety of operations.
Business Intelligence
Historically, business intelligence has been the province of the chief financial officer and the executive suite. Most companies have a set of financial metrics by which they measure their performance, in addition to the balance sheet and the operating income statement. These are shown as bar, line or pie charts; are usually brought together as a set of linked reports; and are still printed out and bound together in many companies. As an example, one of our clients still has a set of 14 key business metrics printed in a document called “The Whitebook,” and it is still assembled manually by people in the various departments that “own” the underlying data. In addition to financial data, we also include project-related information in this intelligence domain, since many industrial companies — including oil and gas companies — do most of their work and cost accounting at the task and project level, rather than by departments. One of the most common causes for financial variations at the bottom line is caused by project-related cost-and-schedule overruns. Many major cost headaches are also caused by inefficient processes, described below.
Process Intelligence
Process intelligence deals with understanding the business implications of work processes, so as to continually improve their efficiency and effectiveness. Efficiency denotes how well a work process is executed by the people and systems that
support it. Effectiveness tells us whether the work process is actually the right process design and whether it needs to be executed at all. To implement process intelligence, a capable workflow or business process management system needs to routinely define and manage work processes. Oil and gas companies are becoming much more rigorous about managing workflows as they create standard, repeatable processes to improve their business efficiency and speed training of new employees. Standard processes also improve the companies’ ability to conduct audits for internal and external regulatory requirements to ensure that important steps were not missed. These systems also define work metrics so that process performance can be tracked over time. Standard processes also contribute to worker and environmental safety, since they can be tested, validated and deployed to a variety of workers across many operating locations.
Asset Intelligence
Companies are measured in many cases by the financial returns they get by employing their assets. In our definition, assets can include people, equipment, facilities, tools and materials. Return on Assets (ROA) is a financial measure commonly used in the business intelligence domain. What we define as the asset intelligence domain is the status, location and movement of enterprise assets as a key part of understanding the basic operations of the business. Being able to know where key assets are deployed, their condition and where they are going next, and when they will arrive is a key element of many important business processes, such as supply chain, procurement, personnel, safety, scheduling, space planning, and transportation. In addition, being able to track expensive equipment ensures that it is protected from pilfering or being stolen.
Certainly this is even more important in the fast-paced operations of shale plays, where drilling rigs are digging new wells every couple of weeks and moved to a new location that may be determined just a few days before the move. A major player in this market may have 200 drilling rigs operating all the time. All the other drilling assets — including drill pipe and collars, pumps, drill bits, personnel trailers, and trucks — must be tracked and moved as well. This corresponds to what would be called in manufacturing a “lean operation,” where a premium is put on having minimal inventories of hard assets and moving them to the needed location “just in time.” The ability to plan and re-plan quickly is required so that the unexpected changes to the location or status of key assets doesn’t make the integrated schedule unable to be executed. Remember the military adage “No plan survives beyond first contact with the enemy.”
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People Intelligence
The term human resources is used for managing a company’s personnel assets, but the management systems are typically transactional in nature, such processing promotions, issuing checks, hiring and firing. There are usually separate systems for personnel, training management, expense processing and performance evaluation. The term people intelligence is more about seeing an integrated view of data from these systems. For example, a vice president of operations might be interested in a rise in the accident rate at a particular site. He or she would want to know which employees were involved in the accidents, so as to understand what work roles might be having higher rates of incidents or accidents. The executive could also see what training these employees were supposed to have received and whether they had actually completed it. He or she could also learn whether particular operations managers were involved in more accidents than the norm, so that they could be retrained or put into different roles. These views would require information to be assembled from a number of back-end systems.
The vice president of operations could also use people intelligence in concert with asset intelligence and facility security to make sure that all workers have the proper certifications and training to enter various areas of the facility. In addition, the executive could create regrouping areas where, in the event of an incident, workers could be safely evacuated and rapidly accounted for. Such areas would have electronic “fences” that can read employee badges in open areas.
The Customer Dimension
Upstream companies don’t normally think much about the customer dimension, since in many cases they are selling their petroleum products to other divisions of their own companies or into an open market to brokers. In this regard, they don’t generally treat these customers as valued long-term relationships. Some companies have decided to buy refineries and process plants from upstream companies that are divesting their downstream businesses, especially in the United States. But as global exploration and production (E&P) companies sell off their downstream assets to other companies, they need to seriously consider the needs of these new customers. Downstream companies want consistent quality, stable prices, on-time deliveries and a low-cost buyer/seller relationship from the upstream companies. Upstream companies may not have the information they need to meet these new expectations.
But potentially more important are the new customers of the upstream enterprise, which include government regulators, agencies that lease properties to them, and local community groups concerned about the health and environmental risks attributed to the spike in exploration and drilling for shale gas and oil in the United States, Europe and other countries.
Following the BP Deepwater Horizon disaster in the Gulf of Mexico, U.S. government regulators were sharply criticized for not monitoring the drilling activities more closely. This criticism has resulted in a large reorganization of what was the Minerals Management Service into what are now called the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE). The reorganization has created a whole new set of additional laws and regulations that must be reported against during each phase of the project, requiring drilling contractors and E&P companies to increase the depth and tempo of their interactions with the agency. They also require certification by independent experts. The new regulations also require the CEO of the drilling contractor to “sign on the line” and acknowledge his or her personal responsibility that all safety and environmental laws and regulations have been met. All of these new requirements will increase pressure on companies to be able to find and accurately report on their regulatory response activities much faster and more broadly than they have had to do in the past.
7 As the pace of drilling in the U.S. and European shale increases, upstream companies and service companies find themselves at the heart of a controversy about the risk of environmental harm that hydraulic fracturing (fracking) of the rock might cause. Residents in Pavilion, WY, have complained of water quality problems since the 1990s, and recent fracking activities have seemingly made the problem worse. In December 2011, the Environmental Protection Agency (EPA) concluded that at least some of the water contamination in Pavilion was caused by fracking operations, though they also pointed to contamination from waste pits in the same area. Community and environmental activist groups in most states where shale gas drilling is occurring have voiced similar concerns. In the Marcellus shale areas of Pennsylvania, residents have made such claims, although the EPA found no evidence of contamination after reviewing hundreds of wells. One of the key differences is that the fracking zones in Pennsylvania are 7,000 feet below the aquifers, while in the Pavilion case, the aquifers are close to the frack zones — only 1,000 feet below the surface. These issues have been elevated to a perception problem that is forcing operators and fracking service companies to be more open to community concerns and requests for information. The consequences of not treating community residents as “customers” and keeping them properly informed will be shut-downs of fracking operations with the loss of jobs and vast new sources of energy.
The Big Data Problem
All companies are seeing huge increases in the amounts of data that they are being required to manage. Chevron reported in 2009 that the amount of data it has to manage doubles every two years. In the 2011 CSC Leading Edge Forum (LEF) report, “Data rEvolution,” the authors state that analyst group IDC predicts that the digital universe will be 44 times bigger in 2020 than it was in 2009, totaling a staggering 35 zettabytes. EMC reports that the number of customers storing a petabyte or more of data will grow from 1,000 (reached in 2010) to 100,000 before the end of the decade. By 2012, it expects that some customers will be storing exabytes (1,000 petabytes) of information. In 2010, Gartner reported that enterprise data growth will be 650 percent over the next five years, and that 80 percent of that will be unstructured. Hardware and storage platforms such as the Oracle Exadata have largely kept up with the demand for physical storage, and high-speed data access, but finding, assembling and visualizing the data has gotten even more difficult.
One of the forms of unstructured data contributing greatly to this flood is real-time data. The application of real-real-time data used to be thought of as limited to a NASA mission control room, with engineers monitoring displays of rocket launches and space flight missions. But now, real-time data is collected in virtually every type of business and its use is growing exponentially. Insurance companies are collecting data in real time for fraud detection. The U.S. Dept. of Homeland Security is collecting and analyzing people-and-threat data around the clock from a variety of sources. A National Science Foundation project is instrumenting part of the Pacific Ocean to gather physical, geologic, chemical and biological data at a rate of 8 GBps. Examples of the exploding use of real-time data can be found in virtually every industry, from agriculture to zoology.
Oil and gas companies are now collecting field-drilling and production data in staggering amounts. Chevron has instrumented 15,000 wells in one field alone in the California San Joaquin Valley, and real-time instrumentation and control systems are a fundamental element of the “digital oil field” architectures being deployed widely throughout the world in new ventures. These instrumentation and control systems allow a reservoir to be closely monitored in real time, allowing the impacts of changes in production plans to be rapidly measured, and control valves on individual well completions can be adjusted to optimize the recovery of products from the reservoir over its life. This information can be shared in a production collaboration center in such a way that the operation of the entire field is optimized, not just the production of one set of wells.
In managing complex industrial operations, cross-functional teams are now the rule. In a real-time drilling information center in the North Sea, producer Statoil monitors drilling activities on several widely dispersed drilling rigs. The collaboration center has a team of experts in drilling, geology, geo-steering, pressure control and facilities engineering who use real-time data and three-way videoconferencing feeds to oversee the macro view of the operations and enable Statoil’s senior team to leverage their skills across multiple operating locations. When problems develop, the experts can be conferenced in to help solve problems quickly and safely. It is not difficult to imagine how this oversight might have helped prevent the BP disaster in the Gulf of Mexico. The onsite rig crew ignored obvious warning signs and bypassed key safety systems to expedite shutting down drilling operations and moving to the next worksite. They could not see the total view of all the problem indications and ignored individual warning signs as insignificant. The shut-in process that they used did not result in the well’s being left in a safe condition, which would have been obvious to an onshore oversight team with the right data monitoring the situation.
9 However, it is not enough to build digital data networks, and install large-screen televisions and collaboration facilities. New process flows are required to make this new way of working effective. Business rules that combine different data feeds and examine the interconnectivity of their responses will be needed to predict and guide decisions. Questions must be addressed, such as, “How much autonomy do the people on the rig actually have? When does the remote team assume command of the operations?” With the new processes, behaviors have to be changed as well, so that workers will behave in a collaborative way, as opposed to the old way where they worked independently and, in some cases, didn’t report problems or failures that occurred.
The changes in how large-scale oil and gas operations over the past five years are significant and long-ranging. Most large E&P companies led with new technology, including downhole sensors and controls, new data communication networks, and dedicated control-and-operations information centers. In many cases, these efforts resulted in strong organizational resistance and lack of adoption. But now, industry leaders recognize that technology change on its own cannot provide the benefits they seek, because field people will not use the technology in the optimal manner. Field workers must be provided with, and trained in, new work processes that reflect the leverage available from the technology and the changes in roles and responsibilities that come with these shifts. In the future, the industry must also deal with the massive quantities of new unstructured data in all forms, including real-time. This data was previously unavailable and has not been effectively integrated either into the technology for data management and display, or into the work processes. Further, there are challenges in making organizations more collaborative in sharing and jointly analyzing the masses of data, while still providing an effective decision-making process. This might be the most difficult challenge of all, but it is essential to create the productivity improvements that justify the investments that have been made.
Learn more about CSC’s Energy expertise at www.csc.com/energy, or contact us at 800.272.0081.
CSC’s Energy Division
With over 20 years of experience in Upstream sector, CSC’s Energy Division helps companies address the most pressing industry issues and opportunities associated with cybersecurity, supply chain optimization, mobility, business intelligence, globalization and collaboration. From logistics to asset movement and multi-company financial reporting, we help companies take control of their performance by enabling the entire value chain to make critical, cost-effective business decisions that boost growth and profitability. Learn more about CSC’s compelling combination of Energy industry expertise, world-class analytics and supply chain capabilities, innovative mobility solutions and global strategic alliances at www.csc.com/energy.
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