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BigData in Oil and Natural Gas industries

Guest blog post by VINU KIRAN .S

     In this article I am going to discuss about complete view of using big data and analytics techniques over vast geological survey data, as they are applied to the oil and gas industry. There is always a need for optimization in the oil and gas exploration and production which includes finding, locating, producing and distribution stages as the gathered data shows how data analytics can provide such optimization. This improves exploration, development, production and renewal of oil and gas resources.

     By using large-scale geological data, applying statistical and quantitative analysis, exploratory and predictive modeling, and fact-based management apporaches over those data, we can gather productive information for decision making also we can gather insights over oil and gas operations.

As found on internet, the three major issues that face the oil and gas industry during the exploration and production stages are:

Data management, These includes storage of a large-scale structured and unstructured of data that can be used for analysis and effective retrieval of information using analysis and statistical methods,

Quantification of data, Thiese includes the application of statistical and data analytics methods for making predictions and determining the insights over those predicted values.

Risk assessment, These includes predictive analysis of the gathered data with known risks that are realized using mathematical models to know how to properly deal with unknown risks

We know that Oil companies are using sensors that are located throughout the oil-field in a distributed manner, high-end communication devices, and data-mining techniques to monitor and track the field drilling operations remotely. The aim is to use real-time data to make better decisions and predict problems.

     As Oil is not found in big, cavernous pools in the ground. It resides in layers of rock, stored in the tiny pores between the grains of rock. Much of the rock containing oil is tighter than the surface on which your computer currently sits. Further, oil is found in areas that have structurally trapped the oil and gas – there is no way out. Without a structural trap, oil and gas commonly migrates throughout the rock, resulting in lower pressures and uneconomic deposits. All of the geological components play an important role; in drilling wells, all components are technically challenging. These data can be gathered pictographically or by means of sensor that results in a large-scale unstructured data.


     In these kinds of industries, organizations must apply new technologies and processes that will capture and transform a large-scale unstructured data such as geographical images, sensor data as well as the structured data into actionable insight to improve exploration and production value and yield while enhancing safety and protecting the environment. Oil-Well and field operations are equipped with sensor instruments to capture reading-data to get a 0view of equipment performance and well productivity data including reservoir, well, facilities and export data.

Leading, analytics-driven oil and gas organizations are connecting people with trusted information to predict business outcomes, and to make real-time decisions that help them outperform their competitors. Regardless of the wealth of data and content available today, decision makers are often starved for true insight.


     The processes and decisions related to oil and natural gas exploration, development and production generate large amounts of data. The data volume grows daily. With new data acquisition, processing and storage solutions – and the development of new devices to track a wider group reservoirs in a field, machinery and employees performance.

     While surfing on interner the following are three big oil industry problems that consume money and produce data, where the BigData, data mining and analytic techniques can give their better insights to reduce the risk factors:

1. Oil is hard to find. Reservoirs are generally 5,000 to 35,000 feet below the Earth’s surface. Low-resolution imaging and expensive well logs (after the wells are drilled) are the only options for finding and describing the reservoirs. Rock is complex for fluids to move through to the wellbore, and the fluids themselves are complex and have many different physical properties.

2. Oil is expensive to produce. The large amount science, machinery and manpower required to produce a barrel of oil must be done profitably, taking into account cost, quantity and market availability.

3. Drilling for oil presents potential environmental and human safety concerns that must be addressed.
Finding and producing oil involves many specialized scientific domains (i.e., geophysics, geology and engineering), each solving important parts of the equation. When combined, these components describe a localized system containing hydrocarbons. Each localized system (reservoir) has a unique recipe for getting the most out of the ground profitably and safely

So, we can conclude that the oil and gas industry has an opportunity to capitalize on big data analytics solutions. Now the oil and gas industry are in need for educating big data on the types of data the industry captures in order to utilize the existing data in faster, smarter ways that focus on helping find and produce more hydrocarbons, at lower costs in economically sound and environmentally friendly ways

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