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Introduction Petroleum data analytics is a solid engineering application of data science in petroleum-engineering-related problems. The engineering application of data science is defined as the use of artificial intelligence and machine learning to model physical phenomena purely based on facts (e.g., field measurements and data). The main objective of this technology is the complete avoidance of assumptions, simplifications, preconceived notions, and biases. One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). While using actual field measurements as the main building blocks of modeling physical phenomena, petroleum data analytics incorporates several types of machine-learning algorithms, including artificial neural networks, fuzzy set theory, and evolutionary computing.
Traditional statistics has been around for more than a century. Actually, the term was coined in Germany in 1749. If its connection with probability theory (randomness) is taken into account, then its history may even go as far back as the 16th century. Nevertheless, the point is that, unlike artificial intelligence (AI) and machine learning (ML), traditional statistics is not a new technology. In order to develop a better understanding of the fundamental differences between these two technologies, one should start with the seminal paper written by Leo Breiman, a well-known professor of statistics from University of Berkeley (Statistical Modeling: The Two Cultures).