Data Analytics in Oil and Gas Industry

  • Manisha Sahu
  • Jul 07, 2021
  • Big Data
Data Analytics in Oil and Gas Industry title banner

The data in the oil and gas sector is one of the key components. Most businesses handle a vast amount of data every day. To analyze them they strive to find new solutions. The system configures several sensors and the Earth's RFID infrastructure to collect data. 

 

Structured, unstructured, and also semi-structured data are collected. They can cope with the enormous volume of data via combining historical data and also real-time data from many sensors.

 

It's only rudimentary data. It's worth it, but it truly can't be utilized if unpolished. It is true that data is of little importance until and unless it is broken down into bits and examined.

 

The oil sector does not seem to be far behind when the globe becomes more open to big-data advantages. If the enormous amount of data is only stored, it will have little value and thus has to be recognized, aggregated, saved, analyzed, and improved for its usefulness.

 

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Big Data Analytics Use Cases In The Oil & Gas Industry

 

Whether it is the improvement of ROI or safety measures, data analytics has a major influence on the O & G industry. Data Analytics in  O & G is a business that depends significantly on data to operate its processes, which has proved beneficial in several areas of this industry in advanced analytics. The industry's ever-growing reliance on data and the need to move frontiers in the research and production process have given the importance of state-of-the-art analytics in the O&G business.

 

Ensuring human safety

 

The security of workers and the environment, in particular, during the drilling process, is one of the key concerns for the oil and gas sector. When the employees are extracted, there is always a danger that hazardous fumes may temporarily or fatally affect them. Today, O&G firms use Big Data and Predictive Analytics to locate new sources of oil and gas without having to perform potentially dangerous procedures to decrease this risk.

 

 

Reducing Production Costs

 

There are variously internal and external elements, from drilling wells to pipelines, which affect the oil and gas company production costs. Big data analysis may be utilized to increase production efficiency and to save costs through several scenarios.

 

 For example, rock analysis is used to determine an adequate site for digging oil wells, pairing down-holes with nearby oil production data can enable oil companies to adjust their boiling strategy in real-time.

 

The data analytics potential can raise oil and gas productivity by 6%-8%, according to Bain & Company.

 

 

Predictive And Preventive Maintenance 

 

Using predictive analysis, O&G businesses have been able to build simulations that forecast maintenance occurrences. Predictive maintenance lowers the expense of unpredictable reactive and downtime maintenance. 

 

These forecasts can assist businesses to keep a step ahead by optimizing downtimes for large-scale maintenance operations. Predictive maintenance may be carried out to enhance the dependability of the gas compression system - an essential component in many offshore installations which results in major downtime losses.

 

Algorithms may be used to forecast breakdowns with more than 70 percent accuracy in the gas compressor train and to enhance productivity.

 

In addition to predictive maintenance, businesses might examine the adoption of a precautionary maintenance strategy involving frequent equipment examination and replacement.

 

Upstream, Midstream, And Downstream Optimization

 

Big data analysis helps to simplify key oil and gas operations in the three areas – upstream, midstream, and downstream exploration, drilling, production, and delivery.

 

 

Upstream 

 

Manage seismic data

 

The upstream analytics commence with seismic data (collected by sensors) acquired over a possible region of interest in the petroleum search. When the data is collected, a drilling location is processed and evaluated. Seismic data can also be integrated with other data sets for the determination of oil and gas amounts in petroleum reservoirs (history data of a firm on past drilling operations, research data, etc).

 

Optimize drilling processes

 

One method to maximize drilling is to customize predictive models that predict possible fails in equipment. The equipment has sensors for collecting data during drilling operations as a starting point. This data is carried via machine learning algorithms, combined with the material information (model, operating parameters, etc.) to detect patterns of use that are likely to end in breakdowns.

 

Improve reservoir engineering

 

Downhole sensors may be used to collect information essential for the firms to improve the production of reservoirs, including temperature sensors, sound sensors, pressure sensors, etc. 

 

Data analytics were also utilized to increase the management application of reservoirs and to improve reservoir modeling through production data analysis. In order to detect the underlying pattern in production data and predict production performance, engineers have used sophisticated tests to provide a smart projection and flow technique.


The images shows use cases of oil and gas industry in big data analytics, which include upstream, midstream and downstream.

Use cases In the Oil and Gas Industry


Midstream

 

Logistics is an immensely difficult problem when it comes to the oil sector. Their primary aim is to carry gas and oil securely without any risk. Companies use sensor analytics to ensure secure energy product logistics. With large-scale data analytics, organizations examine tanker and pipeline sensor data to detect malformations such as stress corrosion, fatigue fractures, earthquake displacement, etc.

 

Downstream

 

Oil and gas companies may use big data analytics to minimize downtimes and refining equipment maintenance costs, boosting asset management. As a first stage, a comparison of the past and present operational data of the equipment analyze its performance. 

 

The performance estimate is further adjusted according to the device's end-of-life criteria and failure situations. Finally, the estimated efficiency of the equipment is represented and provided to maintenance experts to decide, say, if this asset will be replaced.

 

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Challenges Of implementing Big data in Oil and Gas industry

 

  • The data transmission from the field to database processing plants depending on data type, the volume of data, and data protocols are one of the crucial problems in digital oil fields.

  •  The other concern is the frequency and quality of the data gathered.

  •  The physics of the situation is also a serious difficulty to understand. Expert oil engineers should work with data scientists to make use of the proper big data technologies and to identify answers to the various petroleum engineering challenges.

  • Experts must specialize in open-source models, cloud technologies, computer technology, and iterative methods for development. Shell, for example, has around 70 employees in the data analysis department working full-time, with the participation of more than hundreds of people worldwide.

 

 

Characteristics of big data in oil and gas

 

Volume

 

It refers to seismic data or the amount of information a firm has.

 

Variety

 

This refers to many data models which can be organized, unstructured, such as pictures, videos, and semistructured, gathered from pools via different sensors.


The image shows the characteristics  of big data in oil and gas industry, the characteristics of oil and gas industry in big data are  volume, variety, velocity, veracity and value. It shows how useful data is extracted.

    Characteristics Of Big Data In Oil And Gas Industry


Velocity

 

It refers to data streaming gathered by drilling equipment in real-time.

 

Veracity

 

Improve data quality by employing various combined models or the combination of data from several phases like drilling, seismic, and manufacturing.

 

Value

 

Extraction of meaningful data is done after going through previous steps.

 

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What do the oil and gas industry experts say?

 

The Oil & Gas sector, along with rapid development, is expected to be very demanding and rapidly increasing since technological advances are taking place around us. The oil & gas industry mainly includes crude oil, natural gas, gas liquids, petrochemical plants, petroleum distributors, retail outlets, gas, diesel, and lubricants.

 

The fast adoption of technology progress across this business, including the increased use of diverse drilling equipment, cost optimization, oil and gas analytics, etc., have been key contributors to the expansion of the oil and gas industry. The expansion of this sector will also be driven by a growing dependency on energy sources and their growing consumption.

 

Statista states that the United Kingdom is the world's largest oil-consuming country and the world's largest natural gas-consuming nation with a production of 669 million metric tonnes of oil.

 

 

Conclusion

 

So far, we have seen the oil and gas industry's use cases, obstacles, how large-scale analytics are being carried out in oil, and what experts think.

 

Advanced analytics and IoT certainly provide a number of advantages and enable the oil businesses to get a competitive edge. Other advantages of sophisticated analytics include increased production and oil recovery rates, better operations, innovation in exploration, and predictive maintenance. 

 

The petroleum & gas industry is one of the largest industries in the global economy. The population is rising across the world and the demand for oil and gas is growing tremendously. This is how they satisfy the demand and supply and the operational difficulties of the oil and gas analytics industry specialists.

 

The oil business has significant benefits as a resource-based industry. It not only covers the extraction and extraction of crude oil, but also the worldwide operations of exploring, extracting, refining, transporting, and selling petroleum products.

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