With the recent introduction of digital technologies in exploration, drilling and production, the oil and gas industry has become more data-driven.
Seismic and micro-seismic data analysis, improved reservoir characterization and simulation, reduced drilling time and increased drilling safety, optimized production pump performance, improved petrochemical asset management, advanced shipping and transportation, and improving workplace safety is one of the big data applications in the oil and gas industry
Despite recent interest in applying big data analytics in the oil and gas industry, challenges remain, mainly due to the lack of commercial support and low awareness of big data in the industry.
How Big Data is Increasing Productivity for the Oil and Gas Industry
In the 1990s, the oil and gas industry focused on data integration. How do you get all the information in one place and make it available to geoscientists and engineers engaged in hydrocarbon exploration and processing?
Since the turn of the century, technology development has largely focused on software that integrates with core disciplines to accelerate legacy workflows.
The industry has had many great engineers, but the concept of data science is new and should be considered alongside petrophysical, geophysical, and engineering scientists.
With most offshore assemblies operating at 77% capacity, companies are turning to big data and analytics as new ways to improve processes and efficiency.
The vast amount of continuous data generated from upstream, midstream and downstream oil and gas can be quickly processed and analyzed to gain new insights to prevent equipment failures and improve efficiency.
For example, by connecting the Internet of Things (IoT) to offshore devices, operators can control lifecycles and other factors that can affect production, such as: B. Wave height, temperature and humidity, tracked and monitored.
How Big Data Analytics Can Help the Energy Sector
Big data analytics helps optimize key oil and gas operations such as exploration, drilling, production, and delivery across all three sectors: upstream, midstream, and downstream
Automating well development and monitoring processes for companies in the oil and gas industry, and leveraging all of the experience in building and implementing big data solutions, the industry has realized they could apply big data advanced analytics upstream
Manage Seismic Data
Upstream exploration begins with the acquisition of seismic data (collected by sensors) in an area of potential interest for petroleum resource exploration. Once the data is collected, it is processed and analyzed to determine the location of the mine.
To find out how much oil and gas is available in an oil field, seismic data, historical data about the company’s past performance of drilling activities, exploration data, etc. It can also be combined with other datasets such as
Optimise Drilling Processes
One approach to optimizing drilling operations is to adapt predictive models to account for potential equipment failures. First, the rig is equipped with sensors that collect data during drilling. This device metadata (model, operating system, etc.) is driven by machine learning algorithms to identify vulnerabilities that can go awry.
Improve Reservoir Engineering
Various downhole sensors (temperature sensors, acoustic sensors, pressure sensors, etc.) can collect the data companies need to improve reservoir production.
For example, companies can develop reservoir management applications to gain timely and actionable information about pressure and temperature changes in reservoirs using big data analytics, use Flow and Sound to increase insight and control of your operations and improve the efficiency and effectiveness of your repository
Midstream Sector Benefit from Big Data
The logistics of the oil industry are very complex and the key is to transport oil and gas with a minimum of risk. Companies are using sensor analytics to ensure the logistical security of power generation.
Predictive maintenance software analyzes data from pipeline and tanker sensors to detect anomalies (fatigue cracks, stress corrosion cracking, seismic movement, etc.) and prevent accidents.
Downstream Sector Implementing Big Data
Oil and gas companies can use big data predictive analytics to reduce downtime and equipment maintenance costs, thereby improving asset management. As a first step, find out how your device is performing by comparing past performance data with current performance data where they focus even more on performance prediction.
Finally, the calculated performance of the system is displayed graphically and presented to the maintenance specialists so that they can decide, for example, to change this property.
Increase Efficiency with Automation and AI
Advances through AI will be large and small, with potential applications across the energy industry and manufacturing.Example: Surveys of marine assets are often done manually by an inspector with note paper and pencil and then translated into a blank report.
Today, this inspector can record location, image, measurement, condition assessment and relief on a portable device and the results can be made available to the facility manager during the disembarkation when the inspector has finished his coffee break at the accommodation.
Data is stored in a live database, linked to predictive maintenance scopes and accessible via a 3D representation of the platform, allowing anyone to access it instead of being locked into reports on the platform.
Key Challenges of Implementing Big Data in Oil and Gas:
One of the key challenges in the digital oilfield is the transmission of data from the oilfield to data processing facilities, depending on the data type, data volume and data protocol.
Another issue is the frequency and efficiency of data collection
Another key challenge is a thorough understanding of the physics of the problem. Experienced petroleum engineers should work with data scientists to find solutions to problems in the petroleum industry using the right big data tools
Professionals must specialize in open source models, cloud technologies, pervasive computing, and iterative development methodologies. Shell, for example, has about 70 full-time employees in its data analytics department and hundreds of temporary employees around the world.
Oil and Gas: What Blockchain Offers
Blockchain in the oil and gas industry offers businesses a number of benefits, starting with near real-time recording and visibility of transactions between participants, ultimately leading to reduced risk. Data integration helps eliminate the possibility of double spending, fraud or tampering.
The use of blockchain in the oil and gas industry is increasing, opening up new opportunities to improve quality and efficiency
Conclusion: Data is the New Oil
The gas and oil industry is now educating itself on big data analysis regarding the types and large volumes of data the industry collects to help discover and produce more hydrocarbons, economically sound and environmentally friendly. Using enormous amounts of data and turing that data into insights will giv companies the edge.
Quantitative research is developing rapidly. Fully autonomous control systems will soon be deployed at various oil and gas companies’ facilities. This is based on the fact that many business leaders see big data as a solution to run their operations. Oil and gas industries that embrace advanced data analytics are likely to be industry leaders in the future.
If you want to learn more about big data analytics get in touch to see how we can connect you with some experts.