Oil-Gas Industry and Big Data Analytics: How Data Analytics is Impacting Oil & Gas Industry

Data analytics has found its usage in multiple domains. It’s mainly because of the sheer amount of information that data holds today. If used properly, data analytics can solve complex problems, predict events, provide better insights about an organization’s work, etc.

Big data analytics predicts trends, reduces operational costs, and directs strategic investments.

The financial, insurance, technology, and, recently, healthcare industries have immensely benefited from adopting data analytics in their day-to-day workings. However, data analytics in the oil and gas industry has revolutionized its workings. Analytics, especially big data analytics in oil and gas, has made profound changes in the industry that have had an overall positive impact.

This article discusses the various challenges and benefits of big data analytics in oil and gas. To proceed, let’s first understand what big data analytics is.

What is Big Data Analytics?

The world has seen a digital revolution where more and more work is being conducted online, i.e., from buying groceries to warfare. This activity can now be tracked, monitored, and stored. Storing all this activity has led to the concept of big data, i.e., large datasets that are otherwise difficult to manage.

There are six main characteristics, also known as the six V of big data. These include-

1. Volume: The high quantity of data that is stored on the systems.
2. Variety: The different types of data. Big data contains everything from structured tables and semi-structured XML files to unstructured audio, video, and text.
3. Velocity: The speed at which the data is generated is high when dealing with big data. Today, 5 exabytes of data are generated in two days.
4. Veracity: Data quality, i.e., the useful information leading to effective decision-making, is a hallmark of big data. If used properly, big data can solve highly complex problems.
5. Value: The return on investment in big data analytics is huge.
6. Variability: Big data changes its shape and form during processing and lifecycle, which is also a peculiar characteristic of Big Data.

Also read: Top 12 Big Data Skills You Must Have In 2024 and Beyond

Before going into further details of big data analytics in oil and gas, we have a data analytics learning opportunity for you –

If big data analytics is crucial for the oil and gas industry, then this must mean that this industry can generate big data. Let’s understand how big data becomes available in the oil and gas industry.

Big Data in the Oil and Gas Industry

To perform big data analytics in the oil and gas industry, the data generated by the industry should have at least a bunch of big data characteristics discussed above. Let’s understand one by one what characteristics are showcased by the data generated by the oil & gas industry.

• Volume

The big data in the oil and gas industry is generated due to the advancements made in the data recording sensors. These sensors allow for better data collection during the explorations, drilling, and prediction procedures.

Today, data analytics in the petroleum industry is made possible due to abundant seismic and micro-seismic data in the oil and gas industry. However, the bulk of the volume of data is generated by GPS coordinates, information from sensors on exploration and extraction machines, weather services, and other measuring devices. 

• Variety

As discussed in the previous section, the data can be structured, semi-structured, or unstructured in big data. The oil and gas industry generates structured data through applications dealing with surveys, exploration information, and other production-related details. The semi and unstructured data comes from site imaging, emails, market feeds, etc.

• Velocity

Oil industries put emphasis on real-time decision-making as the risk of money and life is significant in this industry. This is why the oil industry emphasizes integrating and synthesizing diverse data sources, causing the velocity of data generation to become substantial.

• Veracity

The intelligent use of big data analytics in the oil and gas industry can help perform various operations such as seismic processing, reservoir modeling, and sensor calibration that aid in different stages of the oil exploration, production, transportation, and delivery processes.

• Variability

As mentioned earlier, data in the oil industry can come in various shapes and forms. The data can be in images and videos as the earth’s surface is scanned for oil, or the sensors and other surveys can also generate traditional structured numeric data.

• Value

Lastly, investing in big data analytics provides immense value to the oil industry. It helps in the navigation, visualization, and discovery of oil, improving drilling processes to reduce cost, improve safety, enhance production, etc.

Therefore, the data generated by this industry has all the characteristics of big data, providing a massive potential for big data analytics in oil and gas. However, you should first be aware of the challenges the oil industry faces before you have an in-depth understanding of how oil and gas data analytics helps it.

Also read: Understanding the Basics of Data Veracity in Big Data

Understanding Oil Industry Problems

In the oil and gas industry, analytics offers benefits similar to those in other sectors. Utilizing data, big data analytics, and data science aids in quantifying uncertainties, uncovering hidden patterns, accelerating processing times, predicting trends, understanding customer and market behaviors, and addressing complex issues that would otherwise be challenging to resolve.

Big Data analytics in the oil and gas industry is beneficial. This is because of this industry’s severe challenges and risks that big data analytics helps mitigate. Of the many challenges faced, data analytics in the petroleum industry addresses the following significant challenges-

• Scarcity of Oil

Oil is difficult to locate. The oil reservoirs are typically found 5,000 to 35,000 feet below the earth’s surface, making them hard to find. The only method to locate them is by taking low-resolution imaging and expensive well logs.

Conducting methods to find and describe reservoirs becomes feasible after drilling wells. The identification process is further complicated because rocks are considered complex for fluid movement to the wellbore, presenting challenges with multiple physical properties.

• High Cost

Oil is an expensive commodity, and a lot of science, engineering, and workforce are required to produce oil. Given the cost, quantity, and availability of oil, the companies involved in this industry must identify methods to stay profitable.

• Environmental Hazards

Right now, oil has a bad reputation. It’s not only due to the carbon emission that the burning of oil produces but also the fact that the drilling required for extracting oil causes severe safety hazards for the individual working at the site and the environmental situation of the site itself. 

• Involvement of multiple domains

The oil industry requires professionals from numerous fields such as engineering, geology, geophysics, environmentalists, and now data scientists. This makes the operations conducted by the oil industry challenging as they have to coordinate with individuals who belong to various fields.

Data analytics in the oil and gas industry tries to solve all these operational issues by reducing bottlenecks and increasing efficiency in oil and gas exploration, drilling, production, and delivery processes.

Let’s now discuss how analytics of big data in oil and gas has been beneficial for this industry.

Benefits of Big Data Analytics in the Oil Sector

Big data analytics has benefited the oil and gas industry in three main areas. These are the upstream, midstream, and downstream processes. Each process is an integral part of the industry, and data analytics helps optimize them.

• Upstream

The upstream process in oil and gas operations refers to the  discovery and production of oil and gas . Many activities are done in the upstream area, wherein big data analytics plays a crucial role.

Let’s explore these activities and understand how data analytics becomes helpful.

1) Exploration and discovery

Seismic data is crucial for oil and gas exploration, providing insights into the subsurface and guiding energy source identification. Big data analytics is essential for efficient gas exploration, enabling strategic decision-making and risk mitigation against financial, temporal, and environmental losses. Big data analytics here thus helps in the following ways-

1. Verifying hypotheses: By analyzing historical drilling and production data, big data analytics helps geophysicists and geologists validate hypotheses, saving costs and enabling exploration in environmentally restricted areas.
2. Integration of diverse data: Big data analytics is vital for combining geospatial data, reports, and syndicated feeds in reservoir exploration. This enables the identification of seismic traces, and visualization helps pinpoint high-potential oil sources.
3. Scientific model improvement: The continuous use of big data analytics allows data scientists to create enhanced scientific models by integrating historical and real-time data, including large datasets like Petabyte seismic data and Mud Logging.

2) High Accuracy in Drilling Methods and Oil Exploration

After identifying oil or gas sources, companies employ highly accurate drilling methods to bring them to the surface. Utilizing big data analytics, they assess conditions, identify anomalies affecting drilling, and aim for real-time information to maximize asset performance, prevent failures, and optimize productivity.

Specifically, big data analytics helps the drilling process in the following ways-

• Optimizing drilling parameters involves considering real-time sensory data from the drills. Models created using existing data and geological measurements are incorporated into the drilling processes, such as in shale development.
• Early identification of anomalies is attempted to prevent events like blowouts and kicks. Using big data analytics helps increase the accuracy and safety of the drilling process.
• Using big data analytics, the drilling process is optimized to identify factors that can adversely impact the operation, potentially leading to increased non-productive time (NPT).
• Big data analytics allows for creating scalable computing technologies to identify the optimal cost for the drilling operation.
• The real-time data analysis from the drilling sensor allows for predictive modeling that enables engineers to make informed decisions at short notice.
• Predictive modeling also helps predict the time needed for drill maintenance and other loss of time due to downtime. Prior knowledge of this lost time can allow companies to consider this when making their strategies.

3) Improve reservoir engineering

Oil and gas companies’ future depends on the availability of these fossil fuels. Therefore companies need to identify reservoirs and understand their viability constantly—big data analytics help conduct detailed studies to identify commercially viable prospects with minimal risks. To make energy more affordable and sustainable, we use big data tools to understand the earth’s subsurface better.

Lastly, big data analytics optimizes the number of active wellheads and drilling resources, ensuring that it avoids over-drilling to minimize waste and ecological harm.

4) Production accounting

Data analytics is essential in optimizing oil and gas production by forecasting future events and improving flow methods. It enables the identification of patterns in historical production data, minimizing costs by uncovering loopholes and leakages.

Examples include using data analytics to identify rock in oil well drilling, assess reservoir levels, and predict production. Another application is optimizing electric submersible pumps (ESPs) by analyzing historical data, forecasting, and preventing emergencies like overheating and unsuccessful start-ups. Overall, these activities aid production engineers in effectively reducing production costs.

• Midstream

 The midstream activities in the oil and gas industry refer mainly to the transportation of oil and gas, i.e., logistics . Big data analytics is used to enhance shipping performance. For example, to improve the performance of ships and reduce greenhouse emissions, big data analytics helps by predicting the propulsion power.

Big data analytics is essential for planning pipelines and infrastructure to transport oil from sources to refineries and pumping stations. Its significance in logistics makes it a critical tool for oil and gas companies, given the highly flammable nature of the transported material.

Big data analytics helps prevent accidents by predicting and detecting anomalies and issues, such as stress corrosion and fatigue cracks in pipes and trucks, along with early detection of seismic movements.

• Downstream

 The downstream oil and gas activities mainly involve refining and selling oil and gas . Let’s start with the involvement of big data in the refining activity.

1) Refining

Various companies are using big data analytics to enhance their refining techniques. These include testing a new combination of chemicals to treat wells under slick water. Big data analytics that directly impact the refining capabilities of a company are being implemented to improve petrochemical asset management.

2) Health & Safety

The responsibility for the health and safety of the individuals working in the oil and gas industry is with the companies that employ them. Big data analytics is becoming increasingly beneficial in this regard.

Accumulated and analyzed historical data of various injury-causing accidents help identify patterns and trends to mitigate the risk of working in this field. Additionally, big data analytics analyzes data gathered from safety inspections conducted over the years and incorporates them into predictive analytics to identify and implement safety indicators.

3) Predictive And Preventive Maintenance 

Another important downstream activity is maintenance, where big data analytics can and does play a significant role. Big data analytics helps predict maintenance incidences and allows companies to take preventive measures.

These include, for example, forecasting gas compressor performance and identifying conditions for malfunction and tentative service life so that engineers can take the information into account and look for ways to refine the performance of the equipment.

Real-Life Examples

Apart from all these examples of the benefits of the involvement of big data analytics in the upstream, midstream, and downstream activities of the oil and gas industry, several real-life examples can further expand your understanding of the benefits.

• In Real-time and Highly Cost-effective

Due to the use of big data, production can be improved by 6 to 8%. This is possible as analyzing large amounts of data in real-time is now easy, which makes near-real-time visualization possible, helping to make the production process highly cost-effective.

• Risk Reduction and Better Decision Making

While rocks across the regions may vary, they can be similar in many ways. Therefore lessons learned from one place can be applied to other similar areas. Today’s data analytics allows us to understand issues from one place and forecast them for another, helping reduce risk and allowing for better and instant decision-making.

• Ensures efficient performance of machines

Shell, for instance, collaborates with HP on sensors to discern sub-surface details, optimizing drilling efforts. This understanding aids in locating prime drilling spots, setting parameters like drill bit steering and RPM, enhancing shale stimulation, and improving transport operations. Such advancements boost the efficiency of machinery across oil and gas production processes.

Other advantages of big data analytics in the oil and gas industry include increased logistics efficiency, reduced net carbon footprint, increased human safety, improved offshore operations, etc.

Given the many benefits of using big data analytics in the oil and gas industry, it’s no surprise that several oil companies are experimenting with this technology. Many software companies are also developing tools and technologies that can benefit this industry.

Oil and IT Companies in Big Data Analytics

As you would have understood by now, there are numerous benefits to applying big data analytics in the operations of the oil and gas industry. However, as of today, the involvement of big data analytics in these industries is only at an experimental stage. In the next section, we will discuss the reasons for this. But for now, let’s look at the various oil companies making efforts and developing pilot projects to test this technology.

Oil Companies

A few renowned oil and gas companies are gaining experience in big data analytics tools. A few of these companies are the following-

1. Chevron: The company has used Hadoop to develop a proof of concept for performing seismic data processing to reduce the costs of water drillships by efficiently identifying oil reservoirs.
2. BP: This famous oil and gas company has been working on a ‘Field of Future’ project to apply big data analytics to enhance drilling efficiency and security control of overseas drilling.
3. Shell: Shell, a British oil and gas company, utilizes seismic sensory data and employs Hadoop in the Amazon VPC (Virtual Private Cloud) to establish a ‘digital oilfield of the future,’ ensuring continuous optimization 24×7.
4. ENI: This Italian energy company collaborates with the supercomputer HPC5. The goal is to enhance the accuracy of underground rock studies, thereby reducing the margin of error and the time between reservoir identification and production.

IT Companies

As big data analytics is primarily an IT affair, various IT companies also create tools and technologies to help the oil and gas industries.

1. Cloudera: This American software company is engaged in a Hadoop project, which seeks to integrate Apache Hadoop with seismic unix and deploy it on the cloud. This project aims to provide cloud-based services that enable on-demand global seismic data.
2. PointCross: The company aims to use Hadoop and NoSQL to create seismic and drilling data servers. These servers will have large amounts of data that the geophysicist can access on demand.

Numerous companies are applying big data analytics in the oil and gas industry. Hence, it is no surprise that these companies face multiple challenges in implementing this technology. A few of these challenges are discussed next.

Applying Big Data in the Oil Industry: Key Considerations

There are several challenges that the oil and gas industry faces when trying to implement big data analytics in their daily operations.

Some of the common challenges are the following-

1. There is a high financial cost associated with dealing with data. This includes various data management activities such as data recording, storage, maintenance, and analysis.
2. Transitioning into the oil industry poses challenges for data scientists due to the unique blend of data ownership, specialized expertise in data science, and a profound grasp of the physics involved in oil and gas operations. This combination makes it a complex transition for professionals.
3. The data available in the oil and gas industry is unique and peculiar. Hence, it isn’t easy to transfer data from the oil and gas fields to data processing centers.
4. The technology currently available faces various limitations concerning data recording sensors. Machines like drills operate in extremely harsh conditions below the earth’s surface, making it challenging to gather data from onboard sensors due to the risk of malfunction or breakdown.
5. The oil and gas industry also suffers from the issue of infrequent data recording and poor data recording quality, which makes applying big data analytics in the industry difficult.
6. It is challenging for non-IT companies like those based in oil and gas extraction to specialize in cloud technologies, machine learning, artificial intelligence, open source models, and the various associated computer technologies.
7. The last yet one of the biggest challenges is the lack of awareness and business support for implementing big data solutions in the oil and gas industry.

While it is evident that big data analytics encounters various challenges in gaining acceptance and widespread application in the oil and gas industry, the future appears promising due to the numerous advantages and ongoing initiatives in this field.

Conclusion

Like various other industries, the oil and gas industry also increasingly understands the benefits of big data analytics. Big data analytics helps other industries make informed decisions possible; it also helps the oil and gas industry by using a variety of data such as seismic data, drilling rigs, frack performance data, production rates, etc.

The effective analysis of this data allows the oil and gas industry to optimize its processes. The transition to big data analytics in performing the numerous upstream, midstream, and downstream oil and gas activities is slow as companies lack the required skills, human resources, and capabilities.

However, given the benefit of identification of potential oil sources, efficient oil production, reduction in cost and risk, and better compliance and decision-making, it is highly likely that big data analytics will become a big part of oil and gas companies in the near future.

FAQs: 

• Can data scientists work in the oil and gas industry?

Yes, a data scientist can work in the oil and gas industry. As the benefits of using big data analytics are being realized by companies operating in this field, the demand for data scientists has been on the rise. To work as a data scientist in the oil and gas industry, one needs to master the following-

1. Proficiency in oil and gas industry operations.
2. Skills in programming languages like Python, R, Hadoop, and Spark for implementing solutions.
3. Essential theoretical knowledge, particularly in statistics for hypothesis testing.
4. Familiarity with machine learning and deep learning algorithms to develop optimization models for various processes.

• How is data analytics used in the oil and gas industry?

Data Analytics is used in the oil and gas industry to help the industry in numerous ways-

1. Improve the transportation process
2. Better estimate the pressure, volume, and temperature during the oil extraction process
3. Reducing drilling time to lower the cost
4. Predict the location of oil pockets to maximize profits and estimate the amount of oil available there for drilling
5. Predict maintenance cost and time to form better strategies

• How can you benefit from big data analytics in the oil and gas industry?

Big data analytics is increasingly valuable for oil and gas companies, creating opportunities for individuals with relevant skills. If you have a background in oil and gas or wish to upskill, mastering big data analytics can make you a valuable asset.

The industry’s rapid embrace of data analytics highlights its crucial role. If you are intrigued by its applications across various fields or are considering a career in big data analytics, please feel free to reach out for more information.