The Application of Deep Learning in Pore Pressure Prediction and Reservoir Optimization: A Brief Review

Abstract

Accurately predicting pore pressure and optimizing reservoirs in the oil and gas industry is crucial for the exploration and production of hydrocarbon reservoirs. Traditional geophysical methods of pore pressure prediction and reservoir optimization require extensive manual effort and may not fully utilize available data. However, in order to surmount these constraints, deep learning has revolutionized these procedures by engaging in intricate pattern recognition, feature extraction, and predictive modelling. Deep learning models such as Artificial neural network, convolution neural network, Pore-net, FCN, DeepLab V3 +, LSTM, and BP can capture complex patterns those traditional methods might miss. Despite a lack of recorded information in wells, deep learning has significantly reduce uncertainty in pore pressure prediction when information is insufficient. In pore pressure prediction and reservoir optimization, deep learning models can analyse a vast amount of seismic, well log, and geological data to accurately predict pore pressure distribution in subsurface formations and can assist in making informed decisions about production strategies. This helps maximize hydrocarbon recovery, minimize operational costs, and extend the productive life of the reservoir, with better-informed choices, reduced uncertainties, and optimized hydrocarbon recovery from subsurface reservoirs, geoscientists and reservoir engineers can make confident decisions that positively impact the industry. Despite ongoing obstacles such as scarcity of data in developing countries and the complexity of predicting unconventional formations, it is indisputable that utilizing deep learning offers significant advantages. Further research and integration of deep learning with other technologies is recommended in order to facilitate the creation of more efficient approaches for predicting pore pressure and optimizing reservoirs.