Role of Data Analytics in the Oil and Gas Industry

Role of Data Analytics in the Oil and Gas Industry

Role of Data Analytics in the Oil and Gas Industry

The financial results of several companies in the oil and gas (O&G) industry are heavily dependent on the output of complex, capital-intensive process facilities. However, until recently, oil and gas companies lacked the resources and skills needed to operate these assets to their full potential.

When used correctly, advanced analytics can generate returns of up to 30-50 times the investment within a few months of implementation. Furthermore, they can positively change the organizations and fundamental operating structures of O&G manufacturing systems. Accessing and extracting rich information from vast data sets can make the oil industry more profitable and fertile. Let’s observe how the ever-increasing volume of data produced by oil and gas companies can be used to solve these challenges when turned into practical insights.

What role does big data analytics play in the industry ?
Big data analytics helps optimize crucial oil and gas activities such as exploration, drilling, development, and distribution across the three sectors – upstream, midstream, and downstream.

Upstream & Midstream Optimization : The challenges in the oil and gas industry's upstream phase are to boost the efficiency of existing resources while still looking for new resources to ensure the supply of crude oil remains consistent.

Exploration efforts can be enhanced by using Big Data and advanced analytics, productive seismic traces can be detected, and drill accuracy can be improved. Big Data helps with optimized oil recovery rates, optimized production, determining the optimum cost, and assessing new opportunities by predicting future output from available historical data. By using Predictive Analytics in transportation and storage, we can optimize midstream operations.

Human safety and environmental conditions : A significant challenge of the oil and gas industry is the danger to human life and the environment during the drilling process. Workers' health is jeopardized as a result of hazardous pollution generated during the extraction of natural resources.

New resources and rough, remote areas can be detected using Big Data and Predictive Analytics. AI robots can detect equipment faults and send warnings in the event of a gas leak. Safety can be ensured by replacing workers with robots that can operate in hazardous and time-consuming environments, maximizing efficiency and expense.

Decision Making : Unstructured data, such as maintenance records, weather reports, media reports, and so on, can be processed using Natural Learning Processing to help companies make realistic decisions.

Another AI assistant approach is transforming and summarising safety meetings that can help workers in the decision-making of solving a critical problem.

Database Administration : Upstream discovery and development data are highly complicated and contain vast volumes of data. Consequently, we need an effective data storage solution to compile and cleanse data captured and stored from multiple sources

Modern analytics techniques, such as seismic applications, data visualization, and so on, are instrumental in data maintenance. They allow for more efficient time management, the discovery and mapping of new oil reserves, and reduced operating costs.

The oil and gas industry produces a vast volume of organized and unstructured sensor data from upstream, downstream, and midstream fields. Aximind will use Big Data Analytics to explore these data, evaluate the best drilling spot, increase recovery times, prevent injuries, and reduce environmental effects. Big Data Analytics aids in the prevention of injury and the reduction of downtime in high-maintenance equipment. It offers actionable information that can save time during the crucial decision-making phase. Good expertise and strategic judgment while using big data tools will ensure success and reduce error margins.

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