Last week, in “America’s Oil Boom Makes a Comeback With Data Analytics,”
we assured our readers that this is not the end of America’s shale boom. By utilizing data analytics, oil companies can reduce expenditures while simultaneously increasing production. Due to the low price of oil
, this has become mandatory for companies in the oil and shale industry.
Now, lets dig a little deeper and see exactly how this is possible. So what do data analytics do for oil companies?
1) Maximize Well Production Through Optimize Subsurface Mapping
2) Find the Right Balance Between Cost and Production
- Discover the location of the best drilling sites
- Indicate where and how to meet the drill bit
- Determine the best way to stimulate the shale in that specific location
3) Optimize Efficiency of Truck and Rail Operation
- For example, companies will increase the amount of sand they use to hold open the fissures. But is this worth the cost? It used to be, but now, due to the low oil price it is not.
- Find out if other methods are cost efficient.
4) Overall Asset and Operations Optimization
- Improve efficiency of transportation and distribution
- Increase machine efficiency
- Mobile Computing
- Increase Uptime
- Reduce Maintenance
- Reduction in Errors and Rework
- Low-Cost Compliance
“But the single biggest disruption now coming to the shale industry, one that will define the emergence of Shale 2.0, comes not from the individual technologies or digital connectivity, but from the use of big data for radically better asset optimization and operations.” – Mark Mills, senior fellow at the Manhattan Institute think tank.
5) Improve Workforce Productivity
6) Analyze Data From the Well Head to the Gas Pump
- Give the oil and gas operators greater intelligence and insight to make the best and most efficient decisions
- This is the greatest benefit
- Tools, alone cannot accomplish this
Something companies in all industries tend to struggle with in the data world is a lack of consensus on data models and formats. This is the case within single business units, and especially within an entire company. This is an even greater and more common dilemma in the shale industry, due to the grand scale and diversity of its operations. In the oil industry, operations occur in a variety of environments, are very diverse and there is a great deal of data.
Therefore analyzing the disparate data wouldn’t get you very far. Data analytics are only useful when the data is collected and consolidated. However, a data analytics company
, like Cliintel, can gather the data from all of these disparate sources, to conduct the best data analysis possible. The best data analytics lead to the best decisions.
This is why many oil and shale companies are already getting in the data game. There is even a new professional society focused on data-driven petroleum analytics. Companies that have engaged in analytics thus far are operating far more efficiently.