Application of Artificial Intelligence in Petroleum Data Analytics
Using artificial intelligence in petroleum data analytics will improve the performance of the oil and gas industry.
Petroleum data analytics is a solid engineering application of data science in petroleum-engineering-related problems. It is defined as the use of artificial intelligence and machine learning to model physical phenomena and it is purely based on facts like field measurements and data. The main objective of this technology is to completely avoid assumptions, simplifications, preconceived notions, and biases. One of the major characteristics of petroleum data analytics is, its incorporation of explainable artificial intelligence. While using actual field measurements as the main building blocks of modeling physical phenomena, petroleum data analytics incorporates several types of machine-learning algorithms like artificial neural networks, fuzzy set theory, and evolutionary computing. Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable.
In the beginning of the use of artificial intelligence and machine learning, the scientists started asking, how this technology achieves its predictive objectives? The main reason behind the fact is that,the engineering application of artificial intelligence and machine learning, to a large extent, is quite explainable and has to do with the historical problems of the application of traditional statistics in solving engineering-related problems. While traditional statistics’ main solutions are about identification of correlations in the data, engineers and scientists were always interested in causations that are capable of explaining the correlations. This has always been one of the main problems associated with traditional statistics to solve problems. Many data-driven solutions still are referred to as “black box” solutions. Engineering applications of artificial intelligence and machine learning do not generate black-box Solutions.
The time-driven oil field is already expected to tap into 125 billion barrels of oil and this trend may affect the 20,000 companies that are associated with the oil business. Hence, in order to gain competitive advantage, almost all of them will require data analytics to integrate technology throughout the oil and gas lifecycle. With the use of big data, companies can not only cut costs but also capture large data in real time. Such use of analytics can help in improving production by 6%-8%. Big-data analytics can also optimize the subsurface mapping of the best drilling locations, indicate how and where to steer the drill bit, determine the best way to stimulate the shale, and ensure precise truck and rail operations.