Data Science in Energy Management

As is the case with a number of industries, energy companies are incorporating data science in energy management strategies as a way to better manage and conserve energy. From seismic monitoring to temperature measurement and gathering wind information, there’s a constant stream of energy data to uncover, examine and analyze. It is the hope that data science in energy management will lead to more and more smart power options for both the energy companies and consumers.

How Can Data Science Help the Energy Industry?

Energy companies are applying innovative technologies to reduce error margins, compete with some intense competition and work around strict government regulations. The dream of having easy energy can only be realized now by incorporation of data science in energy management, and companies are doing just that.

With good analysis, big data can help the energy industry:

• Discover new sources of energy
• Reduce drilling and exploration costs
• Heighten the efficiency of exploration and increase productivity
• Forecast and prevent accidents during exploration and usage
• Ensure there are no power outages, guaranteeing users of 24-hour energy distribution
• Gauge how people consume energy
• Increase the supply as demand increases
• Enhance energy conservation in homes and industries
• Enhance the process of maintenance and repairs

These measures will help ensure that energy companies will increase their profits and consumers will have energy when they need it.

Data Science in Energy Discovery and Exploration

One example of using data science in energy is through seismology. Energy companies are using seismic monitoring, or seismic surveys, to discover new oil-rich grounds. Using monitoring tools, energy companies are able to assess the profitability of newly discovered oil, using numerous and parallel processing platforms. Data science is widely used to estimate the amount of oil or gas that is yet to be extracted from a well, determine the quality of the soil, investigate any geological anomalies, and to compare historical production data, local oil drilling history, weather and environmental changes to facilitate better drilling and production.

Realtime Metrics in Oil Production

Because billions of dollars are involved in oil production there is a lot of pressure at play. Oil extraction has become a high-tech affair. Picture an oil field where every machine is in communication with the others and they’re all constantly relaying data to the energy company headquarters. Oil rigs are in communication with their operators to maintain a steady production flow. Sensors alert the mine workers of any repairs needed, and compressors notify workers whenever there is a dangerous overload.

This sounds a bit far-fetched, but it is already happening. Chevron has been using smart technology to enhance oil extraction and production processes. Officials call it intelligent-field (i-field). BP has the same kind of technology, and call it Field-of-the-Future, while Royal Dutch Shell uses Smart Field technology. Simply, these are digitalized oil fields that all gather data.

The technologies these companies utilize collect sensor data including temperature, pressure, volume, vibrations and shock. They combine the data with real-time analytics and real-time international communications to enable the energy companies to monitor every step of the oil production process. These steps include the moment oil is discovered, extraction, daily well and machine maintenance, purification and distribution.

By incorporating data science in energy management, energy companies can enjoy profits from every drop of oil extracted while reducing costs. According to Chevron, with a digitized oilfield production, rates can rise by up to 8 percent while discovery rates can increase by up to 6 percent.

Safe Oil Extraction and Usage

Failure in extraction and production of oil and gas is costly. It can cost lives as seen with Exxon Valdez, Deepwater Horizon, and Fukushima. Oil and gas companies cannot afford failure. By incorporating data science in energy management, potential accidents can be detected and prevented before they occur. By using sensors, companies can monitor the life cycle of different components and machines and replace them before they cause a disaster. Some examples are:

• When temperature and pressure go above normal, an alert is relayed to the control center and control measures are taken.
• When the ground on which the well rests is fragile and cannot support the drilling, this is detected, and the process halted until a solution is found.
• When key machines need parts replaced, the operators can have the parts ready.

Data is also collected from weather forecasts, maintenance reports, geologic surveys and video reports. This data is used to detect unusual patterns, determine dangers, and help prevent accidents or catastrophes.

Production of Clean Energy

In addition to oil extraction and maintaining safety within the oil industry, data science is also used in the renewable energy sector. Renewable sources of energy are gaining popularity by the day; they are the future of the energy industry. In the US, for instance, by 2030, about 30 percent of the energy produced should be clean energy.

When harvesting wind energy, wind turbines rotate and can be used to collect wind data. Sensors are fitted on the wind turbines to collect wind speed, pitch, and yawn degrees. The data collected is used to monitor the performance of the turbines, including any failures.

Wind data combined with turbine functioning data, vibrations and acoustic emissions, work order data such as repairs data, and turbine history such as years of productivity, shows the energy company numerous places to enhance efficiency and reduce costs.

Data Science and Energy Consumption Analysis

Most consumers, including private citizens and industries alike, are wasting energy by the way they use it. As such, perhaps one of the areas where data science can help the most is in analyzing energy consumption with the goal of reducing consumption instead of increasing production. Energy consumption data can be collected and analyzed in a way that will lead to better efficiency. With energy efficiency methods in place both domestically and corporately, savings are being reported by the Energy Information Administration. Even as the economy has expanded, energy consumption has reduced greatly from 2014.

Energy companies, homes, and other industries are all working to reduce energy waste. Home systems such as Google Home, Nest Thermometers and sensors are already reducing home energy consumption. And the National Institute of Science and Technology predicts that by 2030 companies will be using smart-grid data analysis to save up to $2 trillion .

As oil is becoming more scarce and as clean energy become more mainstream, there is a clear need to effectively incorporate data from all areas of energy production. From extraction to consumption, data science in energy management is a key element to making energy available, profitable, and safe.

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