Using Machine Learning to Optimize the Distribution of Oil and Gas

Moving petroleum product from production to consumption is challenging.  Managing costs, margins, volumes and resources is key to the success of any midstream organization.   Our client entered the midstream marketplace with a vision of using data as a key differentiator for gathering, processing and management of their pipelines. This strategy set them apart from their competition, thus giving them the ability to analyze oil flows more efficiently.

Management realized the power of predictive analytics by the integration of machine learning. This is where the Datalere data science team got involved and helped this organization optimize their logistics models.  Their first recommendation was to select a cloud platform to support and automate their batch load processes.  Through the use of AWS technologies, the client was able to realize exponential cost and deployment time savings.  Once the solution for ingestion and integration of the data was in place, the Datalere team was able to integrate the advanced algorithms and machine learning models that management needed to recognize key patterns on the fly and take appropriate action.  This has proven to be a key differentiator for this organization as they expand their operations.