Jun-Hua Zhang

This paper presents a novel Bayesian-based method for predicting brittleness. The method involves synthesizing petrophysical data from multiple well cores to establish a joint Gaussian distribution function for shale facies and non-shale facies. Furthermore, Bayesian facies classification is applied to seismic data. The proposed method combines non-shale facies data with Rickman brittleness data to obtain a new brittleness index. The joint Gaussian distribution function and Bayesian classification are utilized to enhance the differentiation of brittleness among different geological bodies. Practical data analysis demonstrates that the new brittleness index effectively increases the contrast in brittleness values between various geological bodies, highlighting target areas of interest. The presented method offers a promising approach for brittleness prediction, leveraging the integration of petrophysical and seismic data through Bayesian techniques. The results suggest its potential applicability in enhancing the characterization and understanding of geological formations.

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R², MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model.