The thought of Upstream Data Management revolves around recognizing, gathering and organizing data, with maintaining appropriate relationships that provide ease of access and retrieval of data. The petroleum industry too depends on large amount of data that is produced through various surveys (Geophysical, Geological). This results in enormous data stored in the company’s databases, if data is managed and utilized appropriately, it can become useful in making better decisions.

The advantage of the data can be determined only if it becomes accessible to the business at the right time. This can be achieved through effective data management, with the purpose of delivering intelligence to the business needs. The Upstream Data Management team requires coupling of Oil and Gas domain experts, and IT experts that have different skills (Petro physicists, Geologists, Geophysicists, Reservoir Engineers, Geoscientists, Data Loaders, and Data analyst), working together for effective data management.

Upstream Data Management Lifecycle

• Data Collection: The first step is to collect raw data from remote locations. This must happen in a standard way (template), where metadata is created after reviewing from the different sources (surveys, manual data collection, satellite images).
• Data Classification: The next step is to align collected data with the industry standard, and classify and categorized accordingly. The first standardization that comes to the mind is for naming conventions, template standards, project standards – coordinate reference system standards, storage/database standards, etc.
• Data Validation: The next phase is to verify and correct the data with the help of domain experts – geophysicists, geologists, reservoir engineers. A lot of data quality activities are performed in this step – reporting daily KPIs, accuracy reports, and reviews. In this phase, businesses work closely with the data management team to ensure goals are achieved.
• Data Upload: The last step is to make information/data available to right stake holders. This requires processed data to be uploaded to different applications. If we judicially follow all the above steps, this data can create a huge difference in making critical business decisions.

A well-executed Data Management lifecycle yields following advantages:

• The Oil & Gas companies are organically so branched out, that it becomes a bottleneck in ensuring and implementing organizational standards. To minimize this gap, we need to instrument a well-executed data management process that will break communication barriers, by providing information/data in a standard format and at a shared location.
• The upstream Oil & Gas projects have large amount of raw data. This huge data going through data management lifecycle with proper cleansing activities and analytics build on top of that can used for critically analyzing information for various operational and production problems. Thus, a lot of many can be saved by early identification of risks.
• It helps in creation of custom workflows that ease out the internal integration between geophysics, geology and engineering data.
• Day-to-day access to the presentable structured and standardized data produces better analytics in online monitoring.
• Cleansed data and centralized availability holds the key for Knowledge Management.

A Data Management approach is very well needed to implement the Data Management lifecycle

It requires an amalgamation of right people, process and technology to deliver the corporate priorities. A successful business decision needs to be backed strongly by a Data-driven process. The vital elements that need to support fruitful data management project would be:

• Plan: Understand the vision of the company, and align that with the role of the data management.
• Design: Identify and set template, standards that need to be followed during each phase. Set and remain focused on milestones that are in line with the vision. It needs deeper involvement with domain experts throughout the process, so that right stakeholders are enabled with right access of data.
• Execute: This is the key, it needs adoption by different business units according to their own needs, and this can be possible only if it allows tailored approach that can be easily configured as per the different business needs.

In today’s world, effective Data Management has bridged the gap of widely dispersed data silos. It has helped create accurate data with consistently that can be made available into the different business applications, which can be accessed at the right time by the right people to make the better decisions.