Data analytics will continue to grow in the oil and gas industry. Globally, the oil sector is not far behind when it comes to taking advantage of big-data advantages. Data is estimated to cost $100 million per day in the oil and gas industry and is mainly used by operators who use SAP, Oracle, or other database applications. Generally speaking, these systems are used to manage the production, transportation, and sale of oil and gas. Innovative ways of leveraging value are being developed by many companies through data science initiatives. Therefore, big data tools have become essential, and data engineering has become one of the most in-demand IT disciplines.

This blog will look at the different uses of big data analytics that the industry can leverage and the reasons why these are important. 

Oil Price Volatility 

It is crucial to understand the volatility of crude oil’s price since it plays such a significant role in global economic activity. A volatile oil price impacts hedging and the assessment of projects whose cash flows are influenced by the commodity’s expected price for risk managers. The exodus of participants, particularly hedge funds and speculators, has made it difficult for companies to hedge against physical oil purchases. Volatility has adversely affected oil-and-gas companies as well as manufacturers and other companies that depend on stable energy markets.

Knowing how to react to fluctuations is crucial from a business perspective, an asset management strategy, a hedging strategy, and a production investment strategy can all be used to manage oil price fluctuations. How does the business respond to the price of oil and its demand when it comes to the price of oil? The parametric method is commonly used to analyze oil and energy markets, where it specifies non-trivial functions of historical values and observable variables. Most of the oil price forecasting models use historical data and trends developed from various KPIs which drive the market condition.  The process of analyzing and predicting the price requires a large amount of data from the respective domains. Most of the time the external factors depend on the various political and environmental challenges, which are hard to predict from the analytics angle. 

Data Accuracy

Making business decisions based on inaccurate data can have a negative impact. It’s for this reason that businesses need to focus on data quality in order to ensure accuracy. Accurate data, relevance, readability, accessibility, completeness, and timeliness are significant factors in evaluating business data quality. Among these factors, accurate data gets frequently regarded as the most crucial of these characteristics to attain.

There has been an increased focus on accuracy in data analytics in the oil and gas industry over the last few years. As a result, a number of ETL and ELT tools are being used to improve operations, maintenance, and time to market. There are a number of new platforms such as Upsolver, and AWS Glue Databrew being introduced into the industry for data analytics as the business begins to realize the value of accuracy. 

Project Management

Being a project manager is difficult when you have to find data that supports why a project failing. It’s even harder in the oil and gas industry, where things are changing at a faster rate than ever before. Due to the fact that there are so many variables involved in the oil and gas industry, this industry is very unique. In the past decade, project management methodology was very different from what it is today when managers were only required to maintain an ongoing project. Now, it has become more about strategizing, collaborating with the project team, agile project management, and managing a project portfolio rather than just managing a project. 

It is possible for modern project managers to simplify these tasks through the use of analytics. Handling high-budget or critical projects needs a bit of extrapolation. Project forecasting often involves analysis of the project performance history and whether the project is likely to be profitable in the future. In order to handle projects with high complexity or ones that are critical, extrapolation is required to update execution data from planning, procurement, and budget to the data lake. When it comes to project health analysis, one of the most commonly used KPIs are SPI and CPI which involves analyzing the project performance data to determine if the project will be healthy or within the budget in the future by using business intelligence and dashboards.

Predictive Maintenance 

Data is collected from selected assets using sensors in predictive maintenance programs. In addition to measuring temperature, vibration, and pressure, sensors can also be used to detect air quality. In order to protect an asset, sensors are installed at strategic points based on the asset’s nature. The industry uses cutting-edge technology, and consequently, the equipment can be very expensive and also at high risk of breaking down.  It is no longer sufficient to prevent individual assets from going down, maintenance must also work to prevent system downtime as well. With the power of Artificial Intelligence Machine Learning, big data, and condition monitoring maintenance can use predictive analytics to make anticipated decisions and avoid asset failure and high reactive maintenance costs.

With advanced analytics, it is possible to increase equipment uptime by up to 20% by predicting failures. Considering organizations with complex assets, that must be reliable, represents a huge impact on profits. 

Production Efficiency

The production landscape is changing towards smart and digital as a result of broader changes in Industry 4.0. An increase in productivity, as well as a reduction in lead times and costs, are expected as a result. According to a recent survey by the International Data Corporation, oil and gas companies are going to continue to increase their spending on digital transformation and data analytics over the next several years.  Operation tracking and supply chain management are two of the major area where big data focus on collecting a massive amount of data to improve its effectiveness through the data warehouse. With connected assets and sensors that can measure, record, and transmit performance in real-time to machine logs which will then capture the data through ETL connectors, There is great value in this data for manufacturers, but the sheer volume of incoming data overwhelms many of them. Through data analytics, they can capture, cleanse, and analyze machine data to uncover insights that can help them improve their operations through business intelligence dashboards.

Downtime

Companies are aware of the importance of reducing downtime and analytics play a big role to reduce downtime to a fraction of what it used to be. Collecting data, whether it be manual or automated, is not enough. The data must be compiled into reports in order to be analyzed. Businesses need an easy way to access and query this data, which is why an automated machine monitoring solution using IoT devices  will make this far easier and more useful than doing it manually as it automatically pulls in the data to run analytics, ML, and allows you to build custom reports and dashboards

There are a variety of reports and metrics you will want to look into in order to better understand why you are experiencing downtimes as you work to mitigate them. With the advent of IoT devices and Artificial intelligence (AI)companies are able to monitor their equipment from a distance to store real-time data in data lakes. When something goes wrong, they can immediately detect it and take the necessary action to reduce the impact of downtime.

Conclusion 

Cloud data warehouses and data lakes, enable businesses to take advantage of the speed, scale, performance, and economics of the cloud. A number of oil and gas companies are also looking to adopt big data cloud platforms. ETL  and ELT tools with an extensive list of pre-built data source connectors, load this data into the cloud data environment and then perform the transformations necessary in order to make data consumable by businesses to make real-time decisions.