Fine Geological Modeling of Fractured Reservoir in Dual-Medium Buried Hill Based on BP Neural Network and Horizontal Well Parameter Correction
Authors: Lv Zuobin , Chen Jianbo , Liu Dong , Liu Huan , Song Jianfang , Lv Yuanzhe 
Published: February 2025

Abstract
JZS Oilfield is an offshore metamorphic rock fractured buried hill oilfield. It was put into development in July 2010.The overall production situation of the oilfield is good, but some problems have been exposed. The main performance is as follows: The difference of water cut rise characteristics and the poor fitting of water cut in some wells affect the prediction accuracy of remaining oil and the adjustment and exploitation potential of the oilfield.
To solve the above problems, a set of fine geological modeling method of dual-medium buried hill reservoir based on BP neural network and horizontal well parameter correction is proposed in this study. The key points of this approach are four aspects. Firstly, the nonlinear fitting and estimation technique of BP neural network is used to reconstruct the fracture density curve based on the conventional logging curve to make up for the lack of well point fracture data due to the absence of imaging logging data in most wells. Secondly, the intersection analysis method is used to optimize the seismic attributes and select a variety of seismic attributes with good correlation with the reconstructed fracture density. Based on BP neural network, the nonlinear relationship between the preferred seismic attributes and the reconstructed fracture density is established, and the seismic trend body of fracture density is generated by the neural network attribute modeling method based on geological model. Then, on the basis of vertical depth treatment of horizontal well trajectories, the matrix porosity regression relation between horizontal wells and directional wells is fitted, and the physical property parameters of horizontal wells are corrected by the regression relation. Finally, on the basis of the above data analysis, the geological modeling of fracture system and matrix system is carried out respectively, and the attribute parameter models of dual-medium reservoir are established.
Multi-information fusion fracture modeling based on BP neural network is an effective technique for fracture system modeling for complex buried-hill fracture reservoirs with very few imaging logging data such as JZS. The use of advanced neural network technique can overcome the adverse influence of insufficient hard data at well points caused by too little imaging logging data to geological modeling, and overcome the limitation of single seismic attribute constraints. By means of multi-attribute nonlinear fusion, the multi-attribute fusion attribute body is generated to predict the development trend of inter-well fractures. Similarly, the quantitative characterization of the matrix system in this type of reservoir is also very important, but because the development wells are mainly horizontal wells, the parameter correction of the matrix system in horizontal wells is very important, and only based on the corrected parameters can the accurate geological model of the matrix system be established.
This study guides the research work of JZS buried hill to stabilize oil production and control water cut in 2024, and puts forward a number of measures and recommendations to help maintain high and stable oil production; Eight potential well locations are proposed, which is expected to increase the recoverable reserves by 1.54 million tons, laying a solid foundation for the adjustment and potential exploitation of the buried hill in the later stage.