Prediction and effectiveness study of fracture distribution based on an artificial neural network technology and production performance data detection method: a case study of Ordovician carbonate formation in the Tahe oilfield, Tarim Basin

Abstract

To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article combines core, conventional logging, imaging logging, and 3D seismic volume data and uses artificial intelligence neural network technology combined with the sequential Gaussian method to establish a linear density volume prediction model for fractures in the study area. In addition, production dynamic data are applied to verify the effectiveness of the fractures. The results show that the artificial intelligence neural network technique is highly applicable with an accuracy of up to 76.8% in predicting the fracture linear density. The fracture linear density is controlled by tectonic activity, and the fracture linear density increases sharply in the areas adjacent to the main faults, the fracture superposition site, and the intersection site of multiple faults, with the fracture linear density reaching 8.65 fractures per meter in study areas. The effective distance of fracture connectivity is mostly concentrated between 550 and 670 m. When the difference in fracture density along the structural ridge is between 2 and 4 fractures/meter, the effectiveness of the fractures is strong.