Abstract
The asset management of an Indonesia's national oil company (NOC) has become complex and challenging because every field acquisition and takeover becomes a new asset (subsidiary). Challenges, such as standard corporate database (e.g., cataloguing), seamless workflow in retrieving massive data, data retention ability, and cartographic reprojection lead to a need for data management optimization and integration, which needs to be securely and reliably managed.
The smart vision methodology migrates architecture from an existing system to a new integrated system. The workflow consists of steps designed to gather information, engage the customer in a collaborative manner, and assemble opportunities into an orderly plan that has strategic alignment and cost-benefit justifications for each case. The approach used by the company refers to the professional petroleum data management (PPDM) data model, which helps move collaboration, integration, and relational database management forward. Deployment of this model results in the standardization of a corporate database. As such, previous different data configurations have a standard cataloguing system, which results in consistent data retrieval.
The smart vision methodology is applied to capture and study the company's existing architecture. A major finding is tremendous data from each of its subsidiaries have their own standards and contain structured and unstructured data, which makes it troublesome for analysis to further determine best business decisions. History data, data retention, and data permission management are arduous and implausible. Distortion of cartographic projection data makes the data nonscalable. To overcome these challenges, the PPDM data model is deployed. Data mapping was performed on 18 of the NOC's subsidiaries' data. Company preference, policies, and regulations are standardized at the corporate level. As a result, an integrated database is being established. Corporate can observe all assets in a single project database, which allows further technical analysis and eliminates data duplication problems, making the data easy to manage. From a user management point of view, the presence of the interpreter source priority (ISP) concept allows user collaboration without disrupting either corporate data or other user's interpretation. Another result is the user's ability to track historical data. This is important for corporate; thereby, users can access not only interpretations results but also other users' knowledge content.
The deployment of this model provides the ability to manage all the seismic, well, and interpretation-related data into one consolidated project regionally in a scalable geographical area and complexity. This allows the company to perform various analytics processes related to all data owned by the company.
