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Aiming at the subjectivity and poor environmental adaptability of traditional reservoir porosity prediction models, as well as the non-linear characteristics of reservoir parameters, the paper proposes a porosity prediction algorithm based on eXtreme Gradient Boosting (XGBoost). The hyperparameters of XGBoost are optimized by Grid_search(GS) and Genetic A lgorithm(GA), and then a highly accurate porosity prediction model is established based on the optimized parameters. By comparing with the MAE, MAPE and RMSE of the five models, it is found that the three performance indicators of the GS+GA-XGBoost model are 0.155880, 0.020500 and 0.527946, which are at least 4–6 times lower than the errors of the other five models. At the same time, the influence of multi-threaded on the XGBoost model is studied. The final experimental results show that on the test set, the prediction results of the GS+GA-XGBoost model are basically consistent with the original porosity, and 4 threads can improve the speed of model establishment and prediction. The model can effectively predict the porosity of the reservoirs in this area, and the model is very popular.

To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm is introduced to construct a model for porosity prediction, and the obtained model is optimized by the grid search method and genetic algorithm. First, the optimal values of the three integer hyperparameters and the ranges of optimal values for the five floating-point hyperparameters of the XGBoost algorithm are determined by the grid search method. Then, the optimal values of the five floating-point hyperparameters of the XGBoost algorithm are determined with the genetic algorithm based on the determined value ranges. In this way, the model for porosity prediction based on the XGBoost algorithm and optimized by the grid search method and genetic algorithm (GS-GA-XGBoost) is constructed, and it has eight hyperparameters with determined optimal values. Compared with other porosity prediction methods, our method solves the problems such as the strong subjectivity and poor generalizability of conventional logging interpretation methods and the insufficient generalization performance of machine learning methods in previous porosity prediction studies, and the accuracy of the constructed model for porosity prediction is also greatly improved. Specifically, the RMSE, MAE and MAPE generated by GS-GA-XGBoost on the test set are 0.527946, 0.155880 and 0.020500 respectively, while those generated by linear regression (LR), support vector regression (SVR), random forest (RF), the XGBoost algorithm with default parameters (XGBoost) and the XGBoost algorithm optimized only by the grid search method (GS-XGBoost) on the test set are 3.535521, 2.801047, 0.375713, and 2.695310, 2.002280, 0.283582, and 1.015801, 0.638878, 0.085942, and 2.781069, 1.860557, 0.293334, and 1.380065, 0.979419, 0.128486, respectively. Finally, the multithread technology is introduced to improve the computational speed of GS-GA-XGBoost. GS-GA-XGBoost provides some technical references for the construction of porosity prediction models for oil fields in Northern Shaanxi, China, and other regions.

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.