Oil companies can't find oil and gas if they can't find their seismic data. Proper data management can deliver superior business results.

Searching for oil and gas is an inherently risky business, but seismic data - and the right technology to maximize its value - can greatly enhance the productivity and success rates of E&P organizations. However, simply implementing technology without aligning the organization to use it effectively will not work. By comparison, a clear vision for success and an aligned organization enabled by appropriate technology can deliver exceptional results. While this is widely recognized as true for seismic interpretation, few companies recognize there are also huge opportunities in data management.
The seismic data asset
Seismic data is expensive and used in large quantities by E&P companies. In a good year, a large North American independent can spend $75 million to $125 million on 3D seismic data acquisition. After 8 to 10 years, the company may accumulate more than a billion dollars worth of seismic data. Given this investment, it is not difficult to justify purchasing the best available interpretation technology for geoscientists to maximize return on this investment.
Therefore, why do many companies still struggle to justify expenditures to maintain and manage the seismic data asset? The likely answer is that, for interpretation, the linkage from data to "finding" success and business value is clear, while the linkage between managing data and business value is not. At any given time only a small fraction of available E&P data is actively used, but that does not mean the remaining data has lost its value. This raises two key questions:
l what are the industry standard metrics for measuring the value of E&P data management; and
l where should companies look for value?
Huge value opportunity
Data management contributes to business value in several categories, the most direct being loss avoidance. Using 3D seismic data as an example, for an E&P company that has acquired $1 billion in data over the past 10 years:
l how much is in usable condition and where it should be;
l how much is misplaced, corrupted, incomplete or simply missing; and
l what are the industry norms?
Informal surveys indicate 25% to 45% of companies' seismic data is "not where it is supposed to be." If that applies to expensive, relatively recent data, what about 20-year-old log data or 30-year-old cores? No wonder geoscientists at companies without a good master data store report spending up to 70% of their time looking for, validating and manipulating data into usable formats. Twenty-five percent of a billion dollars equals $250 million of lost value.
Another category where the value can be seen is in reducing interpretation cycle time. E&P companies working the same area can exhibit an 8-to-1 differential in the time required to develop a prospect to "approved for drilling." For companies with long interpretation cycles, much of the cycle time is directly attributable to the data management problem. Clearly, companies with prospect generation and interpretation cycle times five to eight times longer than their competitors are at a significant competitive disadvantage.
Another contributor to cycle-time reduction is better management of the active data in the interpretation projects. In one company, a recent data and workflow assessment revealed a 15% opportunity for cycle-time reduction solely attributable to improved seismic data management in the projects. Key technology automatically tracks, backs up and manages multiple versions of seismic data volumes. In addition, seemingly mundane matters such as implementing best practice naming conventions, data loading and quality assurance practices all contribute to faster results and lower costs.
Taking the "initial scavenger hunt for the data," the "clean up bad data" and the "restart the interpretation due to late-arriving data" tasks out of the normal interpretation workflow is key to developing quality prospects in less time. Integrated data management leads to shorter "first seismic to first oil" time, increasing revenues and adding more reserves - potentially hundreds of thousands more barrels of oil equivalent per geoscientist each year.
This geoscientist productivity represents another category of data management business value. When geoscientists' time is freed up from data management activities, prospects can be developed faster, and more prospects can be developed each year by the same staff.
Acknowledging that there are possible objections to this metric, the authors believe it is measurable and has substantial merit. Figure 1 represents actual data from 13 companies' operations in 1997 and 1998.
Data management maturity model
How can a company realize the potential benefits of data management? It depends on a company's starting point. An E&P data management maturity model helps companies understand their capabilities and the appropriate target level of data management performance (Table 1). Using it, companies can evaluate their data management efforts in four areas: process performance, technology support, quality and predictability of results, and value determination.
Base level, or Level I, represents the bare minimum approach to data management. Companies in this category do not treat E&P data management as a corporate function; they treat it as a personal function for the data users. Results achieved by Level I companies typically are attributable to capable people and heroic efforts.
Level II companies have recognized E&P data has value and should be managed. Consequently, they have begun managing it and may have made significant investments. Often they have purchased or built technology solutions for parts of the data management problem and may be realizing increased business value as a result. However, the value of consistent processes and practices as applied to data management is not well-recognized, nor does management strongly support it. One missing element is the ability to capture and replicate internal best practices and technologies across the entire organization.
Level III organizations are not yet common in the oil and gas industry, in part because the integrated technology necessary to enable this level of performance has only been commercially available for a few years. Very few companies have had the necessary expertise and resources to develop it in-house, even if they recognized the value in doing so.
At this time, the authors are not aware of any companies that could be classified as Level IV or V E&P data management organizations.
Alignment process
Organizational realignment is more a product of the high-technology era than any other impetus, and for technology-enabled changes, an operational and organizational realignment process is an absolute requirement. It should:
start with development of a vision;
address the impact of new technologies;
work through the new processes and procedures needed to reach that vision;
meet organizational challenges such as defining new roles and performance measurement systems; and
include executing a detailed and disciplined action plan throughout implementation.
Typical challenges of an effective action plan include communication, common goal-setting and measurement of progress toward the vision. Providing commitment, resources, time, integration support and training mitigates these. Only after moving from vision through action plan does a company have full alignment of its vision, people, processes and technology.
Thanks to the improved E&P data management that results, a company's prospect generation process is simplified and cycle time is reduced. Additionally, returns are increased while risks and uncertainties are reduced. By assessing the potential value, it will become clear that for most companies, data management is a huge value opportunity. Implementing improved data management along with a process for organizational realignment will maximize the percentage of the potential value that becomes realized value.