In previous blog “Big Data: a mysterious giant IT buzzword”, I refer to Gartner’s definition of big data which covers 5 V’s with focus on some specific characteristics of the upstream data;
- Volume – Seismic data acquisition
- Velocity – Real-time streaming data from well-heads, drilling equipment and sensors
- Structured: standard and data models such PPDM, SEG-Y, WITSML, PRODML, RESML, etc.
- Unstructured: images, log curves, well log, maps, audio, video, etc.
- Semi-structured: processed data such analysis, interpretations, daily drilling reports, etc.
- Veracity (Data Management practice to provide accurate and good quality data)
- Pre-processing to identify data anomalies
- Run integrated asset models
- Combination of seismic, drilling and production data
- Faster decision and enhancing production
- Reduce costs, such as Non Productive Time (NPT)
- Reduce risks in the areas of Health, Safety and Environment
- Forecast and planning using predictive analytics
The oil and gas industries generate significant data volume through exploration, development and producing hydrocarbons. The Oil and gas industry conducts advanced geophysics modeling and simulation where 2D, 3D & 4D Seismic generate significant data during exploration phases. Thanks to new technologies, we’re able to gather, integrate and interpret data received from thousands of data-collecting sensors to track any activity happening almost real-time or near real-time (NRT). It means structured, semi-structured and unstructured dataset is growing daily.
The oil and gas industry started to recognize the importance of getting access to accurate data faster to make decision quicker. So far most of the analysis has been done the same way it was historically used within technical disciplines and a relatively small geographical study area. Now, we observe huge potential using in-memory technologies such as SAP HANA and big data to learn much more from the data. We need access to the appropriate technology, tools, and expertise to integrate and synthesize diverse data sources into more manageable format and derive insight from these datasets. With big data analytic solutions, we’re able to manage and control the data volume, the complexity of the data and break the barriers of geography and disciplines to see the big picture. Currently there are handful of companies have adopted big data such as Chevron and Shell, however the future looks promising and we’re expecting a big demand for big data, in-memory technology and analytics solutions. Let’s say it will happen eventually!!!