Prediction of crack width of drilling fluid leakage based on neural network

During the drilling, it is difficult to select the appropriate leakage prevention and plugging methods and materials as the development of reservoir fractures is unknown. Herein, a reservoir crack width prediction method based on neural network was proposed with reference to the actual history data of wells. Firstly, the main influencing factors of reservoir fracture width were explored and ranked by correlation analysis, and seven main influencing factors, including pump pressure, drilling fluid displacement and drilling speed, were selected as the input parameters. The rate of convergence of the model was improved using the additional momentum algorithm and the variable learning rate algorithm, and the model structure was optimized. Secondly, the Genetic Algorithm (GA) and Adaboost algorithm were used to optimize the BP neural network (BPNN), overcoming the problem of its tendency to fall into local minima and improving the prediction accuracy of the model. Finally, an Adaboost-GA-BP neural network prediction model was established to predict the reservoir crack width, with the prediction accuracy analyzed comparatively. The research results show that the parameters correlated with the reservoir crack width include the loss rate and loss amount, pump pressure, drilling fluid displacement, drilling speed, well depth, plastic viscosity and static shear force in a descending order. In addition, the additional momentum algorithm and the variable learning rate algorithm can reduce the sum of train set absolute error at the end of training by 27%, significantly improving the model performance. At the same time, the weight and threshold of the model were optimized by the GA algorithm, and the integrated optimization was realized by the Adaboost algorithm to further improve the prediction accuracy. Thus, the final reservoir crack width prediction model established based on Adaboost-GA-BP neural network has a root mean square error (RMSE) and a correlation coefficient of 18% and 0.98, respectively. Compared with other models such as the random forest, the model proposed herein has a higher accuracy, which could provide some guidance for the calculation of reservoir fracture crack and the preparation of leakage plugging scheme during the process of exploration and development.