Application of Artificial Intelligence Reservoir Prediction Technology in Block S

Abstract The F oil layer in Block S is mainly composed of river sand deposits, distributed in a north-south striped pattern with significant width changes. At present, the average daily oil production of a single well in the block is 0.7t, with a water content of 74.1%, and a significant decrease in production. In order to improve the development effect, it is necessary to conduct research on fine reservoir description technology, predict the distribution of sand bodies between wells, and guide subsequent development adjustments. Due to the significant influence of seismic resolution on conventional inversion methods, the prediction results highly rely on well interpolation, resulting in low accuracy and long cycle of inter well sand body prediction, which cannot meet the needs of development adjustment and potential tapping measures. This article takes the F oil layer in Block S as the research area to carry out artificial intelligence reservoir prediction research. It directly uses logging and seismic information to train artificial intelligence image deep learning, establishes a neural network model that can reflect underground geological characteristics, realizes inter well non interpolation high-resolution reservoir prediction, and can finely characterize thin interbedded sand bodies, guide subsequent oilfield development adjustments and favorable area selection.