Prediction method of shear wave time difference in tight sandstone based on GA-BP neural network
Conducting research on the stability of wellbore and fracturing effects in horizontal wells in tight sandstone reservoirs plays a crucial role in the utilization of shear wave time difference data.Due to the constraints of development costs,there are very few shear wave time difference logging data,which poses great difficulties in studying the mechanical properties of tight sandstone.This study pro-posed a shear wave travel time prediction method for tight sandstone based on the GA-BP neural network using conventional logging data such as well diameter,natural gamma ray,and compressional wave travel time.The GA-BP model and BP model were trained and tested using data from the Chang 7 and Chang 8 sections of well D166 in the Dingbian Oilfield L area,and a comparative analysis of the pre-diction performance of the two models was conducted.The results showed that the GA-BP model was not affected by factors such as well-bore environment,lithology,and sedimentary environment.The average absolute percentage error was 3.109 percentage points smaller than the BP model,that had higher accuracy,stronger generalization ability,and better reliability.This method has practical application value in improving the accuracy of shear wave time difference prediction and lays the foundation for subsequent research.
tight sandstoneshear wave time differenceBP neural networkgenetic algorithmpredication method
万方数据
维普
