A Excellarate client offers oilfield services.
The client needed to simulate the earth data and predict the required terrain parameters, which helped them make forecasts on the earth soil.
The earth data was used as input to understand the patterns from available samples of data and get predictions, which could then be used to detect the earth conditions before drilling into it.
Excellarate’s role was challenging as the team needed to simulate the given input data and analyze the soil conditions. This had to be done well before drilling deep into the mature fields for predicting the exact earth conditions. Excellarate helped the client make predictions on the earth soil which helped in optimizing their drilling performance. The team took this challenge and delivered using Google Maps API, D3 charts, DevOps and Big Data Analytics.
Excellarate’s seasoned technical developers were part of the client’s major R&D team which built an effective solution using various open source technologies in Java/JEE. This provided data federation (to support E&P business processes) from multiple sources and exposed data adhering to the OData specs through REST for consumption by a variety of clients in a consistent way. Eventually the huge parametric data was concatenated, processed and provided through a unified platform to the end user.
The platform helped the client dramatically simplify data access for energy companies with vast amounts of information stored in numerous data silos.
- Provided an easy way for applications to access multiple data sources, enabling cross-domain workflows for better decisions.
- Validated interpretations by comparing data from different sources to reduce risk.
- Specifically built for E&P, took into account carto, units and well-bore calculations for accuracy in spatial data (well locations, seismic surveys, etc.).
- Decreased workflow inefficiencies resulting from frequent reformatting and import/export of data between the different data sources.
- Enabled faster and better decisions by extending existing applications to work with many more data sources.
- Reduced data quality issues that arise due to data duplication across data sources.
- Minimized complications arising from correlating data across multiple data sources, e.g., same geological data spread across different data sources.
The client was very satisfied that they were able to achieve their business goals with the platform the Excellarate team built.
The team build the platform using Java Enterprise edition which eventually exposes different set of heterogeneous data through RESTful Web Services adhering to Microsoft OData V3 specifications in various formats like JSON (Verbose & JSON Lite) and ATOM. The platform used some of the niche technologies and open source projects like JBoss Teiid – Database Virtualization Engine, EclipseLink – Dynamic JPA, OData4j – REST Implementation adhering to OData V3 specs.
The Excellarate team was also able to make many cost-effective suggestions to the client. For example, use of AWS and Vagrant to replicate the staging environments and use of open source tools and libraries for profiling.
Tools and Technology used
- DevOps: Vagrant, AWS
- Testing Automation Framework: Selenium
- Scaling: Mod cluster, Mod_Jk, Evaluation of ActiveMQ clustering using ZooKeeper
- Machine learning: Weka
- Big data: Hadoop 2.0, Hbase, Storm