Recent Application of Machine Learning Algorithms in Petroleum Geology: A brief review

In petroleum geology, machine learning (ML) has shown promise as a method for improving exploration and production. Current uses of ML algorithms in this area have significantly improved data analysis, modeling, and prediction, allowing for better decision-making and cost reductions. ML is utilized for a number of activities, including well-log interpretation, reservoir characterization, seismic data processing, drilling optimization, and production forecasting. The interpretation of well logs is one of the main uses of ML in petroleum geology. Using well logs, ML algorithms can categorize different types of rocks, identify lithology, and calculate porosity and permeability. In order to predict fluid distribution, identify the existence of hydrocarbons, and calculate reservoir parameters like thickness and depth, ML models are also employed for reservoir characterization. To better characterize reservoirs and make well placement decisions, ML algorithms are employed in seismic data processing to locate faults, fractures, and other geological features. ML models have been applied in conventional oil fields and unconventional shale plays in places like the Permian Basin in the United States and the North Sea in Europe. It has also being used in complex geological settings, such as tight gas deposits in the Marcellus Shale and fractured carbonate reserves in the Middle East. By forecasting drilling performance, spotting abnormalities in drilling data, and choosing the most effective drilling parameters, ML is also used to optimize drilling operations. The optimum production techniques are found, the remaining recoverable reserves are estimated, and production rates are predicted using machine learning (ML) in production forecasting. Cost-saving manufacturing optimization is possible with the help of this information. Overall, recent ML applications in petroleum geology have yielded encouraging results, allowing for better decision-making, cost reduction, and increased productivity. The oil and gas sector is expected to see ML approaches play a more significant role as they advance.