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.

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 an integral part of reservoir evaluation, but in the field of reservoir prediction, due to the complex nonlinear relationship between logging parameters and porosity, linear models cannot accurately predict porosity. Therefore, this paper uses machine learning methods that can better handle the relationship between nonlinear logging parameters and porosity to predict porosity. In this paper, logging data from Tarim Oilfield are selected for model testing, and there is a nonlinear relationship between these parameters and porosity. First, the data features of the logging parameters are extracted by the residual network, which uses the "hop connections" method to transform the original data closer to the target variable. In addition, the residual blocks inside the residual network use jump connections, which alleviates the gradient vanishing problem caused by increasing depth in deep neural networks. The dynamic nature of data would necessitate LSTM in the first place. Then, a bidirectional long short-term network (BiLSTM) is used to predict the porosity of the extracted logging data features. Among them, the BiLSTM is composed of two independent reverse LSTMs, which can better solve the nonlinear prediction problem. In order to further improve the accuracy of the model, this paper introduces an attention mechanism to learn by weighting each of the inputs in proportion to their impact on the porosity. The experimental results also show that the data features extracted by the residual neural network can be better used as the input of the BiLSTM model.

The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. 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 parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters.

As a key bridge between logging and seismic data, acoustic (AC) logging data is of great significance for reservoir lithology, physical property analysis, and quantitative evaluation, and completing AC logging data can help to obtain high-resolution inversion profiles, which can provide a reliable basis for reservoir geological interpretation. However, in the actual mining process, the AC logging data is always missing due to instrument failure and borehole collapse in many areas, and re-logging is not only expensive but also difficult to achieve. However, the AC data can be completed by other obtained logging parameters. In this paper, a bidirectional gated recurrent unit network based on the Inception module is developed to complete the AC logging data. The Inception module extracts the logging data features and inputs the extracted logging data features into the bidirectional gated recurrent unit network, which can fully consider the characteristics of the current data and the data before and after the logging sequence to complete the missing AC logging data. Experimental results show that the hybrid model (Inception-BiGRU) has higher accuracy than traditional and widely used series forecasting models (gated recurrent unit network and long short-term memory network), and this method also provides a new idea for the completion of AC logging data.