Realize metadata standards and guidelines which enable stakeholders in the energy industry ("the community") to effectively and efficiently discover, evaluate and retrieve information resources.
Support both proprietary data management needs and exchange of data between and within organizations.
Leverage existing standards to encourage adoption within the community and integration into the business and exploit existing organizational resources needed for governance and long-term maintenance.

Version 1.0 of the EIP standard has been distributed for industry comment and is targeted for release in Q1 2012.

The EIP is based on an established ISO 19115 standard and is implemented as a profile of the standard.

Data Discovery and Recall

Discovery: A user starting a project wishes to discover and identify relevant data from sources outside their company. Information about datasets is required to evaluate fitness for use and maximize its value. Standard metadata associated with the data enables users to search and discover data to locate appropriate, available resources without knowledge of locations, organization or naming conventions of the repositories in which the data are stored.
Recall of Existing Data: A user new to the organization is asked to revisit an old project, and must gather and evaluate the data collected for the project given only information such as the area of interest (AOI) or project name.

Data Evaluation

Evaluation of Data/Fit for Purpose: A user is reviewing prospect information and needs to evaluate data used to develop the prospect using criteria such as vintage, source, quality, accuracy, lineage, etc. Without metadata describing these, the user must seek out others who may be knowledgeable about the data, or make assumptions about data that may or may not be correct.
On-going Data Updates: The velocity model used to process a seismic line is updated based on a new processing method, and all derivative cross sections and maps using the data need to be updated. In this case, updating the dependent data set requires knowledge of the processing lineage, including the complete hierarchy of relevant ancestors, as well as tools, methods and parameters used to process the data.
Data Sharing: A user receiving data from a joint venture partner must be able to evaluate and determine the appropriate use of shared data. To accomplish this, the receiving organization must receive associated metadata along with the data for attributes such as status or quality.

Data Access

Use Constraints: A knowledge worker needs to know the conditions under which they are permitted to access and use a particular dataset. Such conditions might include commercial licenses or government regulations. Metadata to standardize documentation of such use constraints would facilitate access to the information, encouraging and enabling compliance.
Appropriate Use: A user needs to understand the intended or recommended use for a given dataset. Examples of this kind of metadata include scale-appropriateness and vintage.

(Version 1.0 focus areas are highlighted)