Carbonate Fracture-Cavity Reservoirs Prediction Technology Based Deep Learning Model

Paleozoic carbonate rock is the key field of oil-gas exploration in Tarim Basin. Fracture-cavity reservoirs are often developed in carbonate reservoirs in Tarim Oilfield, a large amount of oil and gas resources are dis-tributed in Paleozoic reservoirs with different burial depths and scales, accurately and quickly identifying these fracture-cavity reservoirs is Significant to the oil and gas exploration, development, and production of the Tarim Oilfield. With the continuous development of artificial intelligence technology, machine learning methods have been widely applied in various scenarios of oil and gas exploration and development, bringing new opportunities for the development of carbonate reservoir prediction technology. Based on drilling, logging and seismic data, this study comprehensively analyzes the structure, rock and physical properties of carbonate reservoirs in the study area, exploring the main controlling factors of carbonate reservoirs. On this basis, a sample set corresponding to the fracture-cavity reservoirs in the study area was constructed, by using machine learning methods, a prediction model for carbonate rock fracture-cavity reservoirs has been established, which can intelligently predict carbonate rock fracture and cave reservoirs in the research area. The trained model of carbonate rock fracture and cave reservoir prediction can quickly and accurately identify fracture and cave reservoirs on post stack seismic data. This study demonstrates that methods are based on machine learning can quickly and efficiently predict carbonate rock fracture-cavity reservoirs.