The oil and gas industry has been collecting data for decades, whether it is seismic surveys to information gathered from equipment sensors. And with many companies installing systems to measure production, drilling, and other operations, there is a vital need for real-time analysis of large datasets getting produced and are stored in historians for years. This Data Science has evolved into Big Data which leverages new technologies for handling and processing massive datasets.
The processes and decisions related to oil and gas exploration, development, and production generate large amounts of data and its volume grows daily. It is this kind of data engineering that can rapidly bring together and explore massive sets of structured and unstructured data to uncover hidden patterns, new correlations, trends, customer insights, and other useful business information.
Petroleum engineersand geoscientists spend over half of their time searching, assembling, and analyzing data. Big Data in Oil and Gas is viable for three things mainly - Volume, Variety, and Velocity. Recent research in 2018 conducted with more than 80% of oil and gas companies indicate that Big Data is vital for improving oil and gas exploration, transportation, and processing efficiency. However, it is interesting to note that most of the research work done back in 2012 showed that 70% of oil and gas companies were not familiar then with Big Data and its applications inpetroleum engineering.
The main three big oil and gas industry problems that consume money and produce data are discovery, drilling, and producing oil and gas. Data gathered from refineries and routine safety inspections can be used to develop safety predictive analytics. It is vital to collect the safetyindicator datawithin the company continuously and use them in predictive analytics. The safety indicators which will provide the required data include assessing behaviors and meeting compliance.
As oil and gas companies awake to the potential of analytics, many jobs will be created for data scientists, opening a portal for new applications and ideas to enter the industry.
Machine Learning is a means of building mathematical models to help understand data. Big Data can be used for Supply Analytics, Predictive Maintenance, Proactive EHS, Seismic Analysis, Reservoir Modelling, Drilling Services, and Production management. There are several areas where big data can be used to benefit refining and oil exploration industry:
- Production Optimization
- Supply & Demand Forecasting and Analytics
- Multivariate analysis
- Predictive Maintenance
- Anomaly Detection
- Fault Diagnostics and Prognostics
- Digital Asset Signatures
- Health, Safety & Environment
- Enhanced oil exploration
- New oil prospect identification
- Seismic trace identification
The oil and gas industry must leverage big data in order to utilize the existing data in faster, smarter ways that focus on helping find and produce more hydrocarbons, at lower costs in economically sound and environmentally friendly ways. Data Science is leading to more automation in terms of the energy industry’s consumption and management, thus creating informed energy productivity decisions for both now and into the future. Data Science blended with fundamental science is at the heart of innovation for the Oil & Gas industry.
At iOG Solutions, we specialize in Data Sciences in downstream areas like equipment failure prediction, safety, reliability analytics, and SCM (demand and price prediction) as well as Upstream areas like exploration, discovery, drilling, and production. iOG Solutions believes that the most effective Data Science-based strategies identify business requirements first, and then tailor the infrastructure, data sources, and analytics to support business opportunity.
