The upstream oil and gas industry is facing major challenges in managing the vast amounts of subsurface data generated from its operations. Conventional data management techniques such as physical storage and manual data ingestion are becoming increasingly inefficient, leading to a growing need for more effective data management solutions. Effective data management is critical in enabling exploration teams to make informed decisions and maximize the value of their subsurface data assets. In this article, we will explore various subsurface database solutions for their strengths and weaknesses, with a focus on architecture, deployment types, and integration with the Open Subsurface Data Universe (OSDU) standard. These findings are summarized in the table below:

NoData Management SolutionStrengthWeakness
1Relational databases (e.g., Oracle, SQL Server, PostgreSQL)– Structured data can be easily stored, retrieved and analyzed
– Query and analysis capabilities are robust
– Good data security features
– May not be well-suited for large amounts of unstructured data
– Can be challenging to scale for big data
– May require specialized skills to use effectively
2NoSQL databases (e.g., MongoDB, Cassandra, CouchDB)– Can handle large amounts of unstructured data
– Good scalability for big data
– Flexible data model
– Limited querying and analysis capabilities compared to relational databases
– Data security features may not be as robust as in relational databases
3Cloud-based storage and data management (e.g., AWS, Microsoft Azure, Google Cloud)– Scalability and accessibility of data
– Flexible pricing options based on usage
– Advanced security features
– Dependence on internet connectivity
– Potential for data privacy concerns
– Cost can be higher compared to other options, especially for large amounts of data
4Data lakes– Ability to store large amounts of structured and unstructured data
– Cost-effective compared to traditional data warehousing solutions
– Good scalability for big data
– Lack of structure can make data management challenging
– Data security and privacy can be more difficult to manage
– Can be difficult to integrate with existing systems and processes

Relational databases

Relational databases such as Oracle and SQL Server are commonly used for structured data management. They provide efficient storage, retrieval, and analysis of structured data, and offer robust query and analysis capabilities. However, they may not be ideal for managing large amounts of unstructured data, and may require specialized skills to use effectively.

NoSQL databases

NoSQL databases like MongoDB and Cassandra are designed to handle large amounts of unstructured data. They are highly scalable and provide a flexible data model, making them well-suited for big data. However, NoSQL databases may have limited querying and analysis capabilities compared to relational databases, and their data security features may not be as robust.

Cloud-based storage and data management

Cloud-based solutions such as AWS and Microsoft Azure offer scalability, accessibility, and flexible pricing options based on usage. They also provide advanced security features to protect sensitive data. However, they may require internet connectivity and raise data privacy concerns, and their cost can be higher compared to other options, especially for large amounts of data.

Data lakes

Data lakes are cost-effective, centralized repositories for storing and managing large amounts of structured and unstructured data. They are highly scalable for big data and provide cost savings compared to traditional data warehousing solutions. However, they lack structure, which can make data management challenging, and data security and privacy can be more difficult to manage. Integrating data lakes with existing systems and processes can also be challenging.

The Open Subsurface Data Universe (OSDU)

OSDU is a data management platform designed to provide a standard data architecture and common data services for the exploration and production (E&P) sector. The platform enables companies to securely store, manage, and analyze vast amounts of data from various sources such as well and seismic data, drilling and production operations. By providing a single source of truth for the data, OSDU streamlines data management processes, reduces operational costs, and enables data-driven decisions. It is an open-source platform, allowing companies to contribute to its development and benefit from the innovations of others in the industry.

The i2G Data Management Solution

i2G Corporate Database Solution provides several key features for organizations looking to effectively manage their subsurface data. These include:

  • Enhance data utilization and maximizing efficiency within a unified data environment: the i2G platform provides a centralized location for storing and managing subsurface data, which can help organizations to maximize data utilization and efficiency. With a unified data environment, teams can more easily find and access the data they need, reducing the time and effort required to manage their data.
  • Quick and precise data query by advanced search options: the i2G platform includes advanced search options that allow teams to quickly and precisely query their subsurface data. This helps organizations to get the information they need more quickly, improving their productivity and decision-making ability.
  • Seamless connection between Corporate Database, Project Database and working projects: the i2G platform enables a seamless connection between the corporate database, project databases, and working projects. This allows organizations to easily access the data they need across multiple projects, without having to navigate multiple systems or databases.
  • Easily manage with high security and fully controllable admin system: the i2G platform was developed with high security and a fully controllable admin system, making it easy for organizations to manage their data and ensure the confidentiality and integrity of their information.

The database management system on i2G platform can handle both structured and unstructured data and is being integrated with the Open Subsurface Data Universe (OSDU). This helps organizations to maintain data quality and manage their subsurface data more effectively. The structured database and advanced file management system makes it easier to organize and access the data, while the integration with the OSDU standard allows for easy data sharing and collaboration with other organizations using the same standard. By adapting this standard, i2G helps organizations to streamline their subsurface data management and achieve their exploration and production goals.

Get in touch to know more about our solution:

Previous articleCollaboration Between Revotech International and University of Sharjah
Anh Nguyen is an experienced reservoir geoscientist with a passion for digital transformation in the upstream upstream G&G sector. Throughout his professional career, he has dedicated himself to researching and applying machine learning and artificial intelligence in geoscience. He has been actively involved in the development of the i2G cloud-based data platform (, leveraging his expertise in the field. Through his consulting work, he has demonstrated a proven track record of applying advanced technologies such as effective collaboration, data management, and machine learning and AI.