Optimization of Logging Interpretation Parameters Based on Committee Machine

Aiming at the problems of low precision and low applicability of the selection method of macroscopic capture cross-section parameters of rock skeleton, formation water, oil and gas, and mud in the volume model of remaining oil saturation logging, this study proposes a method for optimizing the capture section parameters based on the committee machine regression model. To begin, we select well sections from well logging data in a reasonable manner, allocate well sections with different component parameters in the same proportion, and construct sample datasets. The basic experts are then selected as multiple regression models, particle swarm optimization, and robust regression methods to train and learn the input parameters. By combining multiple experts, the regression committee machine improves the overall performance of the intelligent model. Finally, the genetic algorithm is used as a combiner to determine the contribution of each basic expert network in the final output, and the optimized parameters are obtained, which are then fed into the volume model to calculate the remaining oil saturation of the newly developed production wells, guiding the perforation and development work. The model is used to evaluate the remaining oil in the X oilfield, and the calculated water saturation matches the oil test results, proving the model’s accuracy and availability. The use of real-world data demonstrates that this method can effectively characterize the four parameter values in the volume model and provide reliable geophysical technical support for the evaluation of remaining oil.