Think you’re losing your memory? Try being an aging E&P company with information overload and scattered data. As data and IT professionals, it can feel like we’re all suffering from E&P data dementia. Many different technologies and data management approaches have had modest success in improving the “corporate well memory”; however, a new technique called Well Master Data Management is proving to make a difference. Yet, if approached incorrectly, at best it can fail, and at worst it can compound the memory problem.

This paper outlines how a major E&P, after two previous attempts, decided to employ a data quality regime to implement a Well Master List. By articulating a tangible business deliverable and using disciplined data quality processes, they succeeded in rewiring their corporate well memory.
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There is no doubt that over the last five years, E&P data managers have become much better at collecting ever larger and disparate data for their users. The volume of E&P data we manage has grown exponentially, yet with this growth there is a downside. Increasingly, our data has become prone to many forms of “data collisions” which result in poor data quality. These “collisions” can result from the introduction of new technologies, standards non-compliance, inconsistent processes, organizational changes, M & A, and other significant business events.

This paper focuses on techniques to minimize the damages that result from these inevitable “collisions”. Learn why using a business case to justify incremental innovation to help manage data quality challenges is not always a recipe for success. Drawing a parallel to the introduction of the automobile seatbelt and the car safety industry, Paul charts a path for data quality tools and the evolution of data quality practices.
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Learning to Trust Your Data: Real Time Quality Integration for G&G Professionals (Presented by Paul Gregory from Intervera and Scott Schneider from Volant Solutions)

Software advances in recent years have changed the way Exploration and Production (E&P) companies utilize and manage geotechnical data. Contemporary geoscience interpretation applications are "data hungry" and able to consume more types of data from diverse sources at a higher rate of speed. Because of the variability of data, technology, data capture methods, and data accuracy levels collected over the extensive operating history of a well's lifecycle, the challenge in the E&P industry remains how can you trust the data tthat you depend upon daily? The more important qyestion might be "how can you NOT trust the data" upon which multi-million dollar drilling decisions are being made on a regular basis?

This paper describes how the application of contemporary software technology can improve the decision-making process by adding a dimension of confidence through data quality as data is moved betwee various geoscience applications and corporate databases.
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Navigating Around the Perfect Storm: Implementing a PPDM “Data Quality Safe Harbour” for Devon Canada

Today’s Exploration & Production (E&P) data professionals face a tidal wave of challenges which can combine together into “the Perfect Storm.” Despite our efforts in using the Public Petroleum Data Model (PPDM 3.7) to help us navigate to calmer seas, poor data quality can still create stormy conditions that undermine users’ confidence. Find out how Intervera’s advanced data quality tools and Volant’s synchronization architecture successfully contributed to a complex data transformation.

This paper uses a real case study of how Devon Canada is leveraging a technique known as the “Data Quality Safe Harbour” to mitigate the risk of ever finding themselves within a ‘Perfect Storm’ situation. This technique deploys pre-built data quality tools into an architecture that deals with data quality in a staged process, but one that can be done ‘on the fly’.
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CSI and Forensic Data Quality Techniques: A Case Study on Resolving a Conversion Gone Bad Data from an acquired company’s well drilling and completions system was converted into the E&P's corporate system. Six months later, the company has missing critical well asset information, incorrect information was attributed to the wrong wells and reporting information was obviously wrong. The users had lost trust in the system, couldn’t determine the cause and had no way of knowing how to fix the problem. The crime to be solved – what happened and what can be done to remedy the situation?

This paper uses a real case study of how an Exploration and Producing (E&P) Oil and Gas company leveraged data quality tools and techniques to complete “a forensic diagnostic” of the problems and identify what steps were needed to resolve the situation. Learn how they identified specific legacy and data conversion issues, worked to develop a strategy and implemented a plan of action to address priority data quality and conversion “crimes.”
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Roadmap for Enhancing E&P Data Quality
This paper provides a roadmap for enhancing data quality through examples of E&P companies who understood the problem, successfully measured risks associated with data quality, and developed solutions that delivered benefits throughout their organizations.
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Achieving Standards Through Automation
Consolidating large data repositories from disparate systems can make the task of standardization seem daunting – in fact the decision to apply standards is often easier than implementing them. This presentation uses examples of challenges E&P companies faced in achieving standards and how a new technology was used to help solve data quality problems and implement PPDM standards
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Four Reasons to Measure Data Quality
This presentation uses surveys and client-based case studies to outline the business risks that exist when companies don't know if E&P data quality is good or bad.
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