About the Summit
The Exploration & Production (E&P) organization requires lots of information and data in order to make the right and accurate decision. Today’s technology has made it possible to obtain high quality information in real time, but this has also increased the associated problems required to properly manage these information and data assets.
Some common issues faced by E&P companies with regards to information and data management include:
- Enterprise Data Management (DM) strategy – Many E&P companies lack a clear enterprise DM strategy. A common scenario in most cases is that DM strategies are driven by specific corporate needs or proposed by external consultants, thereby creating a situation whereby different sub-optimal solutions are implemented in different parts of the organization.
- Resources – There is a scarcity of competent E&P data managers who have a good understanding of DM challenges faced by the industry.
- Data magnitude – The large and growing volume of expensive data continuously generated during E&P data acquisitions makes clear the business need for implementing robust DM strategies across board.
- Lack of standard data formats – E&P data most often are generated and stored in proprietary formats applicable to product vendors. This creates a big challenge with data sharing and integration.
- Lack of clear ownership – E&P engineers are usually more focused on their deliverables and less on data preservation and management. This means data issues fall through the gaps most of the time. E&P engineers need to analyse and own their data and the implementation of a clear enterprise DM strategy will make every individual accountable for their technical data, ensuring that storage and archival processes are not left in the hands of a few data management practitioners.
As they struggle to manage the issues mentioned above, most E&P organizations end up with too much information and little or no management processes to ensure proper harvesting of their data assets. Even where some data management practices are adopted, they may not be implemented fully, leading to security breaches or loss of the potential value that could be harnessed from the various datasets.