Big data, a term used quintessentially refer to large volumes or a large amount of data that can be subjected to analysis in data science in order to yield valuable insights. Big data analytics in oil and gas industry primarily makes way for predictive analytics.
Some might speculate that big data analytics in oil and gas industry does not seem relevant or effective. But, it’s actually a wrong notion. There is a lack of understanding about what actually big data is. Big data does not necessarily encompass data about customers, users, or clients.
Let us try to shed some light for clarity about the topic.
What is Big Data Analytics in Oil and Gas?
Data in its crude form has little value. Only when it is broken down into pieces, analyzed, stored, and perfected, does it become more valuable and insightful. Big Data has created quite a stir in almost all the major industries today. It holds equal importance in the oil and gas industry too.
Big data analytics in oil and gas industry plays an active part in the following aspects:
Big data can be incorporated in all three sectors of the oil and gas industry. Let us try to understand this division in brief.
The companies involved in the oil and gas industry are distributed based on their place in the supply chain as:
- Upstream: Companies that explore, extract, and procure raw materials fall under this category. Their tasks include raw material extraction, identifying deposits or the source, rig operations, machinery rental, etc.
- Midstream: The companies attributed with the three major tasks involving the processing, storing, and transporting materials in the oil and gas industry are included in this category.
- Downstream: The companies that are in the latter part of the supply chain that is closer to the end-user and consumer are included in this category. It involves the stream-lined distribution and marketing of the oil, gas, petroleum, etc. products. Its operations begin post the production phase, up to the point of sale.
Big Data Analytics in Oil and Gas Industry:
The oil and gas industry is such that it has many complex processes from the point of examining an area for its raw materials to final distribution. It has many stages that also lack visibility. So, for a company to flourish, expand, and grow in this industry the most viable solution is making use of analytical tools that can guide and provide some direction in the field.
Based on the division of companies in the industry, we now try to explore the use cases of big data analytics in petroleum industry and oil and gas industry.
For Upstream Companies:
Big data can be leveraged in upstream companies of the oil and gas industry to optimize the existing resources for production and also exploring newer areas where the possibility of finding raw materials exists. Efforts in the direction of improving drill accuracy can also be made.
Analyze Seismic data:
Seismic data is extremely helpful in identifying the traces of oil reservoirs and the potential availability of other petroleum sources. By making use of sensors, such data is collected and it is generated in large amounts too.
Such data is processed and analyzed by making use of intelligent tools and the most optimum drilling place is located. This significantly improves the chances of carrying out rewarding drilling operations which saves up astoundingly in terms of time and money ending up reducing the production costs.
Monitor Drilling Processes:
The struggle does not end at identifying resources, equipment failure, resource downtime, and maintenance costs can hugely impact any oil company. So, with the advent of remarkable technologies like machine learning, there can be algorithms implemented that study the status of equipment in real-time via sensor data and predict the point near which they are expected to create a hurdle and break down.
These algorithms are fed with historical data, operational data, production data, etc. to come up with a conclusion. The more the number of data sets, the more accurate would the result be.
2. For Midstream Companies:
The oil and gas industry deals with products that are high risk to human safety. Its large scale production involves following several protocols by those who are members of the industry. Logistics is another serious concern as transferring material from the oil field, to the final consumer if not done correctly, can be hazardous.
Oil and gas companies for several years have invested in optimizing ways in which they transport their products. With the help of sensor data from tankers, pipelines, or other instruments of transfer, it can be known beforehand of any persisting danger present and can be avoided with immediate action.
3. For Downstream Companies:
Along with the massive implementation of predictive analysis, big data analytics in oil and gas industry is used for asset management, cloud-based services, forecasting revenue, etc.
One of the major reasons why big data is used in any field is that it gives a holistic insight into the current state of the business. Moreover, it commands the output of analytical tools that paint a clear picture of the future of the business if the process is continued in the same manner. Thus, it significantly helps in decision making.
Other features of big data in this field include database management and enforcing human safety in critical environmental conditions.
Big data analytics in oil and gas industry comes with the following set of advantages:
- It reduces the risks involved and strives for better decisions
- Makes the overall operations cost-effective
- Generates massive ROI
- Endows accuracy and improves performance
- Extends the lifespan of machinery involved
Thus, there is no doubt that big data is beneficial and overall very fruitful with its implementation in the oil and gas industry specifically. So, get ready to reap the benefits of this area of data science for your benefit.
Unleash big data potential
With big data analytics, companies transform enormous datasets into sound oil and gas exploration decisions, reduced operational costs, extended equipment lifespan, and lower environmental impact.