Given the enormous expenditures required in offshore exploration and production (E&P) activities, better data management has emerged as a critical for topics ranging from risk reduction to increased revenue.
Yet even as offshore exploration becomes increasingly technology-driven, the question still persists at many companies, "Why and how is data management important within the context of the hundreds of millions of dollars spent on deepwater drilling and production facilities?"
The answer speaks directly to this investment. Take three value categories: risk reduction, dry-hole avoidance, and accurately sized production facilities. With so much capital riding on a single project, it is imperative that each project be provided every possible opportunity to succeed.
Spending just 1-3% of the data acquisition budget for improved data management and data quality can become an insurance policy for the project's economic success.
The numbers are impressive. Linking new data management technology with a fully aligned organization, companies can often double the amount of interpretive time available to geoscientists.
This translates into a greater number of higher quality prospects that can easily add another 0.5-1 million boe of reserves each year/geoscientist.
Additionally, by shortening the interpretation cycle time, companies can go from seismic data gathering to first production, weeks or months faster on every well. Other benefits accrue in terms of recovering value destroyed through lost data and taking advantage of additional lucrative opportunities that would otherwise have been lost.
But how does an integrated data management solution directly tie in with exploration costs and production revenue? Answering this question involves changing some long-held perceptions.
Begin with a fundamental redefinition of data as an asset, not an expense. Add in the fact that timely access to high-quality data is the foundation of E&P success.
Put that redefinition in the framework of all those directly and indirectly involved with offshore E&P, and the real value of data becomes even clearer. Fill in the framework by answering, "Who buys or creates data?"
Typically, it is both the exploration and production organizations. However, "room and board" for the data are usually provided by the information technology (IT) organization, map rooms, and for data that interests them, the individual interpreters.
From there, the network of "data stakeholders" extends to geoscientists, engineers, finance and accounting personnel, asset supervisors, and corporate managers. Finally, the network broadens to external stakeholders including partners, royalty holders, and government regulatory agencies.
Considering how widely E&P data are used, a reasonable question to ask is "Who is responsible for maintaining the corporate data asset?" At this point, for most offshore companies, the data-management story falls apart.
First, different groups typically manage different classes of data, using different naming conventions, business rules, technologies, processes, and procedures.
Second, in an asset-based organization, capital expenditures to maintain data that are not immediately useful become difficult to justify and therefore a target for cost cutting.
Third, companies generally do not fully understand their own internal data management costs and have trouble linking the costs they can identify to business results.
Finally, geoscientists are expensive data managers, both because they are not trained for this work and also because the opportunity cost for using them to manage data, instead of searching for oil and gas, is large.
In 1994, Vastar Resources made the decision to become a major player in the deepwater Gulf of Mexico. Management realized that in order to be successful in this area, it would need to handle, manipulate, and manage efficiently large amounts of 3D seismic data. As a result, it took a look at its processes and technologies for data handling and management.
Vastar looked at what could be measured then developed a baseline set of measurements for the company's then-current performance. For example, one measurement included the calendar time it took fully to load a 3D seismic survey-measured from the time it came in the door.
At that time, it took 16 days on average, regardless of the size of the survey, to prepare it for interpretation. Management decided that was too long, so it set a target of 2 days or fewer from data arrival to data loading completion. Within a 3-month time period, the company achieved this goal.
Vastar management then set a new target and continued reducing this time. Today, Thomas LaHouse, chief geophysicist at Vastar says it takes less than 24 hr to load a newly received seismic survey, regardless of the size of the survey.
Reduction of interpretation cycle times delivers strong financial results. On the front end, companies can tangibly realize cost savings through the development of quicker prospects and faster "first-seismic to first-oil" cycle times.
Such improvements produce more revenues (by quicker production) and more projects. Ultimately, as more reserves are added, and as the company becomes larger, productivity increases exponentially.
Creating business value
As this example shows, E&P data form the basis for making expensive economic decisions. Furthermore, improved data quality and availability through improved data management means that offshore E&P economic decision-making will be better.
As another example shows, after proceeding through a typical evaluation process in the latter part of a prospect life cycle, a point is reached on whether to move forward with a development project or discontinue. If the deepwater block receives the next round of funding, drilling will then require further investment.
By that time, with tens or hundreds of millions of dollars already spent, decisions are often being made using original data that:
- Were acquired far in the past.
- May have been poorly managed since acquisition.
- May never have been validated, or if it was, validated and corrected.
- May not have made available the cleaned-up data for future decision making.
Surely a strong data-management competency would help reduce the risks associated with these decisions and help mitigate the risk in development planning and decision making.
But, how does a company know whether its data are being managed to deliver maximum business value? Until recently, the answer was unclear because the technology needed to support a total integrated data-management solution was unavailable. Today, however, an innovative model brings a company's data-management status into sharper focus.
The E&P Data Management Maturity Model classifies companies on a five-tier scale in descending order (see box): Fully optimized, predictable risk, corporate competency, managed, and base. Results of four key criteria, namely process performance, technology support, quality and predictability of results, and value determination, indicate how effectively a company manages its data.
The fully optimized level characterizes a company whose sophisticated data-management capabilities contribute strongly to an exploration success rate that is close to its development success rate.
At the opposite extreme, base level signifies a company in which data management is not treated as a corporate responsibility and therefore delivers no consistent company-wide value; although individual "craftsmen" may achieve good results through their own unique-and probably undocumented-data management practices.
In conjunction with the E&P Data Management Maturity Model is an Operational & Organizational Alignment Process (Fig. 2), which helps ensure that technology investments made in order to implement a vision are complemented by the necessary process and performance management system changes.
As Fig. 1 shows, a company is fully aligned to realize full value from the technology, only if there are no "gaps" in moving from a clear vision for data management to an effective action plan for deployment. Whenever a gap exists, however, problems with false starts, no change, frustration, anxiety, and confusion can occur.
Because operational and organizational realignment frequently conjures up the idea that there will be a company-wide personnel shake-up, there needs to be a reality check. In fact, properly implemented organizational alignment should not affect either reporting structure or organizational charts.
Its purpose is solely to make the workflow, supported by appropriate technology and the performance management system, function as intended. In other words, technology by itself does not increase either productivity or E&P revenue; effective alignment does.
LaHouse states that "The performance incentives of the data management and applications support personnel are the same as they are for the interpreters-both are rewarded for finding more oil and gas."
Thus, support personnel work closely with the interpreters to find ways to maximize the productivity and success rates of Vastar's finding effort; they are not focused on internal departmental metrics.
"As a percentage of salary, the support personnel have just as much financial upside potential when lots of oil and gas is found as do the interpreters. Alignment is the key" says LaHouse.
Therefore, taking into account both the potential value of data management and the impact that organizational realignment can have on maximizing the return on investment, cost issues jump back into the equation. How can personnel integrate and implement the data management function in the most cost-effective manner?
A growing solution can be provided through shared services whereby entire companies or major business units centralize common services as part of a shared services unit, using either an internal or external service provider. This approach allows each division or business unit to leverage a common technology base and infrastructure of "best practices" for "back-room" functions.
At the same time, shared services allow each division to maintain its own distinct competitive advantages in its core competencies. This concept has been recently extended to data management, whereby data management centers are built and their services offered to multiple clients.
Through this approach, companies do not have to incur the expense of building, staffing, and maintaining their own standalone facilities, an expense that would be uneconomic for all but the largest companies in offshore E&P.
Through shared data-management services, even small or middle-sized independents can afford to have access to skilled people and state-of-the-art technology in order to manage data assets for maximum business value. On a related playing field, implementing an effective integrated data management solution is a clear prerequisite for leveraging companies into e-commerce.
When E&P data are literally managed in "cardboard boxes," it is impossible to link them to e-commerce sites in order to share and trade data about properties, making it impossible for this function to facilitate the exchange of assets. To use E&P data in e-commerce, the data must be kept in good condition, electronically readable, high quality, and well managed.
Another important element of integrated data management is that good data management protects companies from partners that do not. Under operating agreements, companies frequently expect partner approval at various times. This involves a substantial level of trust on a multimillion-dollar investment.
For example, a company may have a 33% working interest in a discovery and may ultimately spend an additional $900 million (gross) for drilling, completion, platform, and pipeline facilities.
Because none of the partners know the true size of the reservoir, however, most will want to proceed with the investment cautiously. Ultimately, the company facing the $300 million net investment will have to base its decision on the available data.
Having timely access to the data allows a partner company to make the most informed decisions possible by itself, reducing the need to rely on the partner's analyses and data. The preferred situation would be for all partners to have joint access to the same well-managed store of data for decision making, instead of relying on their own copies of data.
Integrated data management
Analyzing a company's data-management status through the E&P Data Management Model and realigning the organization to best work with new technology cause benefits to begin flowing to the bottom line.
These include additional benefits beyond adding an additional 0.5-1 million boe/geoscientist of reserves each year or producing first oil weeks or months faster on every well.
- The costs of managing and maintaining the data asset can be defined and brought under control.
- The need to search for or repurchase lost data can be eliminated.
- The need to clean up and interpret the same data sets over and over can be eliminated.
- Future data losses can be prevented.
- Leases can be accurately valued so that bidding can be appropriately conducted.
- During the process of litigation, companies will always know what they owe and to whom.
- Risk can be reduced by narrowing the gap between expected and actual results.
- Better decisions made with up-front access to high-quality data can help avoid drilling dry holes.
- Data management lays the foundation for information management and, in turn, for knowledge management.
John D'Angelo is manager, business transformation services, for Schlumberger GeoQuest. He has been with the company 8 years and also has 14 years experience as a management consultant. D'Angelo holds a BSEE from the Massachusetts Institute of Technology and an MBA from Tulane University.
Bob Troy is senior consultant at Holland & Davis Inc. He has 25 years' E&P experience and is currently working on a project associated with organizational realignment. Troy holds a BS in engineering science from the US Air Force Academy.