In the next series of articles we shift our focus to industry-specific applications of data warehousing. The first industry we will discuss is the energy industry.

Oil and Gas Industry Challenges

The recent challenges facing the oil and gas industry have been well reported. The economic downturn in Asia and Latin America has left crude oil inventories at near capacity, with a corresponding 20-year low market price. Adjusted for inflation, gasoline prices are also at historic lows, but thanks to the lag effect of falling product prices in relation to crude prices, increased gasoline demand and the popularity of less energy efficient sport utility vehicles, downstream companies are still able to realize a slight profit.

In the early 1990s, oil and gas companies reorganized and reengineered, centralized and decentralized their organizations in an effort to cut costs, often resulting in dramatic staff reductions. Now in the late 1990s, BP/Amoco, Exxon/Mobil, Ultramar/Diamond Shamrock/Phillips, UMC/Ocean/Seagull and Shell/Texaco/Star Enterprise are some of the recent mergers, acquisitions and joint ventures whose objectives are asset rationalization and further cost containment, again often resulting in staff reductions.

Data Warehousing in the Oil and Gas Industry

The information technology departments of these companies have borne their share of these cost containment activities. Companies such as Shell have turned their IT organizations into profit centers, while others such as BP have opted for outsourcing these functions. But whatever the IT strategy employed, oil and gas companies have come to realize the data tied up in their ERP and other operational systems is a corporate asset, and data warehousing is the IT infrastructure that enables this asset to become a competitive advantage. Despite this, with few exceptions oil and gas companies have not been early adopters of data warehouse technology. It is just now becoming common for oil and gas companies to have implemented or to be implementing some form of data warehouse/data repository using data warehouse tools and techniques.

Upstream Business Processes

Of major concern to the exploration and production (upstream) business is the cost and associated revenue of finding, producing and delivering hydrocarbons (oil and gas). The performance measure of this process is asset profitability, where the asset is the producing property (lease) or well. The profitability of an asset can vary greatly depending on such factors as the volume of hydrocarbons produced, the market price of hydrocarbons and the expenditures, both capital and operating, invested in the property. Often companies will report this asset profitability monthly in a lease operating statement (LOS). A typical LOS compares total revenues, total expenses and specific performance measures such as product realization and lifting costs for an asset on a monthly, quarterly and yearly basis.

A data warehouse infrastructure is well suited to convert the existing LOS from a paper report of summary data to an analytical tool that will allow users to research and isolate the source of variances quickly. Some of the benefits for the business users from investments in the data warehousing infrastructure are:

  • Using OLAP front-end presentation tools with geographic and organizational hierarchies, data can be seamlessly reported not only at the overall LOS level, but also at the organizational or geographic level appropriate for the user's area of responsibility. To isolate variances, users can drill down and across the hierarchies and easily view revenue, expense, production volume and performance measures for a region, area, field, lease and potentially for an individual well.
  • Workflow, exception detection and alert functionality can proactively push exceptions and warnings to responsible parties. For instance, a manager can be alerted via e-mail to a property's lifting cost exceeding its target value.
  • Using the graphical presentation tools with forecasting algorithms on historical data, trends in production volumes, market prices, lifting costs, etc., can be reported in a meaningful way.

By using data warehouse tools and techniques, the LOS becomes an analytical tool that not only efficiently reports historical data, but also provides forward- thinking, decision support capability.

Downstream Business Processes

Petroleum downstream business processes are more closely related to standard manufacturing and distribution industries or networks. Oil company traders acquire crude oil (purchase feedstocks), refineries process the feedstocks into refined products (manufacturing), pipelines, barges and vessels transport the product to terminals (primary distribution) and tanker trucks transport the product to service stations (secondary distribution). Correspondingly, oil and gas downstream businesses and IT departments are following the example of consumer product and high-tech industries ­ those who live and die by their manufacturing and distribution channels ­ in adopting business processes and strategies to get more value out of their existing manufacturing and distribution assets.

As with retail store chains, oil and gas service station/convenience store marketing presents a compelling demand for a data warehouse with category management analytical capabilities. Convenience stores benefit from the same analysis of the market basket, inventory turns, cross-product purchases and promotion effectiveness and, in many ways, present a more compelling case for category management analysis. Convenience stores have limited shelf space and limited inventory capabilities, making the selection of merchandise categories with a combination of the greatest demand and the highest profit margin crucial. Additionally, service stations connected to convenience stores (or vice versa) have their own unique complications. Marketing strategies to attract fuel customers may take the form of pay-at-the-pump and prepaid gasoline cards that don't necessarily compel a customer to come inside the convenience store. Oil and gas company retail strategies may, in some cases, dictate whether a specific service station location has a convenience store attached or is a convenience store that also sells gasoline.

The Oil and Gas Company Data Warehouse

As with many other industries, financial reporting is a common function of today's oil and gas company data warehouse. Other areas of data analysis providing decision support functionality that should be considered by an oil and gas upstream business include:

  • Drilling statistics, exploration expenses, reserves identified and capital expenditures can be analyzed in an effort to minimize finding and development costs.
  • Production volumes, net working interest, gross working interest, sales revenues and operating expenses can be analyzed in an effort to minimize lifting costs.
  • Capital project plans, capital expenditures, operating expenses, G&A expenses and AFE information can be analyzed to monitor full cycle project costs.

Taking their cue from consumer product and high-tech companies, oil and gas downstream organizations are beginning to see the benefits of adopting decision support processes such as:
Supply Chain Optimization. The new breed of advanced planning and scheduling (APS) vendor offerings have quickly become standard in manufacturing/distribution industries. These packages use APS functionality to optimize supply chain networks, as well as data warehouse technology to monitor and analyze supply chain performance metrics.

Relationship Marketing. The supply chain "customer" in the downstream appears in the wholesale marketing of refined products at the terminal rack. Cross-industry best practices around relationship marketing can identify and maintain the existing customers who generate the most profit.

Trading Performance. Data warehouse and OLAP functionality are ideal for analyzing trading organization performance. Trading profitability can be analyzed across multiple dimensions of geographic region, product, trader, method of transportation, customer, channel of trade, pricing strategy and many others. Product-for-product exchange contract performance can be managed to minimize gross out-of-balance situations, as well as the effectiveness of location trades.

Energy Marketing. A data warehouse infrastructure is well suited to combine data from different commodity marketing transaction systems to create a cross commodity risk management portfolio.

Creating a sustainable competitive advantage in the global energy markets requires information to be used strategically to support the business goals of the energy enterprise. Data warehousing is becoming one of the key strategic programs to help the oil and gas companies capitalize from the challenges and opportunities in this very dynamic industry.

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