Data Standards in Oil & Gas

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1 Data Standards in Oil & Gas September, 2014

2 Business challenges currently impacting data standards in the E&P Marketplace Mergers and acquisitions have caused data challenges in identifying common field assets and JV Partnerships Rapidly growing volumes of information have caused difficulty in identifying & retrieving the right data in the right context A lack of integration across field solutions have caused data quality issues and inefficiency during aggregation & analytics of wells, fields across geographies Increased system capabilities have resulted in a more acute problem with organizations using in memory computing to assess raw data rather than summaries and aggregations Understanding what data management challenges you face can be key to building a meaningful information strategy Deloitte Consulting LLP

3 Master Data Value Lever Value Dimensions Bottom line improvements can be gained by improving data standards Depending on current maturity across organization, processes, and systems, there are specific data elements that can drive shareholder value dimensions. Shareholder Value Revenue Growth Risk Management Asset Efficiency Operating Margin Increase Production Growth Strengthen Pricing and Royalty Mitigate Corporate/ Regulatory Risks Planning and Analysis Grow Investor Trust Asset Use Working Capital Supply Chain Insight F&D Costs optimization of transportation scheduling and drilling plans Field improvements and reliability Reduction in downtime associated with equipment failures Reduce off-contract spend and leakage Location Well Material Vendor Product Asset Equipment Financial (AFE) Hierarchies and Categorization Sub-Surface Sensor Human Resource Land Lease Understanding what value dimensions you are striving for will help identify where to focus your data improvements Deloitte Consulting LLP

4 Leading practices to approach data improvement programs Our experiences with large data management programs has provided us with many lessons learned. If your organization is standards based, you can scale more easily and apply innovative thinking rapidly. 1. Define your and prioritize your scope 2. Focus on high-impact areas and business value that will drive the adoption 3. Start small and evolve additional domains 4. Don t underestimate the effort required as integration of master data into downstream applications is difficult yet refrain from over engineering 5. Establish an enterprise definition and approach for master data management 6. Data quality and governance comes with a cost, be realistic about how much can be done within time and budget constraints 7. Reinforce the message that data quality, the associated processes, and governance are owned by the business IT is a participant and facilitator 8. Data accountability need to be defined with specific roles and responsibilities 9. Conduct data quality assessments early to define complexity factors that drive conversion and testing efforts 10. Regular and targeted communications are essential both within the implementation team and to the business Deloitte Consulting LLP

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