Recent technological improvements have resulted in the daily generation of massive datasets in the oil and gas industry. Applying advanced analytics and artificial intelligence provides oil and gas companies the ability to identify trends and predict events throughout processes. This will enable quick response to disruptions and improve efficiencies.
“Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes” .
What is Big Data? It can simply be defined as unstructured (not organized and text-heavy) and multi-structured data (including different data formats resulting from people/machines interactions) .
Big data offers numerous opportunities for businesses, whether used independently or with existing traditional data. Data scientists, analysts, researchers, and business users can leverage these new data sources for advanced analytics that deliver deeper insights and power innovative big data applications.
Some common techniques include data mining, text analytics, predictive analytics, data visualization, AI, machine learning, statistics, and natural language processing . To handle the volume of data being generated today, open-source software such as Apache, Hadoop, Apache Spark, entire Hadoop ecosystem are designed to ensure flexible data processing and storage .
Characteristics of Big data include Volume, Veracity, Velocity, Value, and Variety. See the image below.
Application of Big data Analytics in the Industry
In the quest to search for what lies below the surface, oil and gas companies have been exposed to large amounts of data. They have complex systems that have evolved over time and now contain disparate data sets. Big data analytics can play a major role in improving operations and productivity across the industry value chain as follows:
- Machine learning tools which are best suited for analyzing seismic data can reveal relationships between recorded data
- Data gathered from different operations through drilling can be applied to conduct various analyses from scheduling to drilling operations  thereby improvingdrilling performance.
- Companies in the business of making big data technologies can develop reservoir management applications. This can be utilized to get timely and actionable information regarding changes in reservoir pressure, temperature, and flow .
- In production engineering, big data analytics can optimize the performance of production pumps.
- Big Data analytics manages risks and enhances safety by improving oil and gas occupational safety.
- Predictive analytics can be usedto reduce downtime and maintenance costs of oil refining equipment, consequently improving asset management.
GACN, as Nigeria’s strategic gas aggregator, continues to explore avenues for leveraging predictive analytics statistical techniques in key activities like price aggregation, gas portfolio management, and network code agency services, to make predictions, create reports and inform key decisions regarding gas pricing, DGSO (domestic gas supply obligation) performance, gas demand trends, portfolio growth projections, etc.
The transition towards implementing big data technologies in work operations may not be easy for many oil companies because of lack of expertise and rigidity towards accepting new work practices but once they get around the limitations, they will reap its full benefits.
In conclusion, data is just like crude; it is valuable, but if unrefined it cannot be put to good use.