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.

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.

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.

In oil exploration and development, many reservoir parameters are very essential for reservoir description, especially porosity. The porosity obtained by indoor experiments is reliable, but human and material resources will be greatly invested. Experts have introduced machine learning into the field of porosity prediction but with the shortcomings of traditional machine learning models, such as hyperparameter abuse and poor network structure. In this paper, a meta-heuristic algorithm (Gray Wolf Optimization algorithm) is introduced to optimize the ESN (echo state neural) network for logging porosity prediction. Tent mapping, a nonlinear control parameter strategy, and PSO (particle swarm optimization) thought are introduced to optimize the Gray Wolf Optimization algorithm to improve the global search accuracy and avoid local optimal solutions. The database is constructed by using logging data and porosity values measured in the laboratory. Five logging curves are used as model input parameters, and porosity is used as the model output parameter. At the same time, three other prediction models (BP neural network, least squares support vector machine, and linear regression) are introduced to compare with the optimized models. The research results show that the improved Gray Wolf Optimization algorithm has more advantages than the ordinary Gray Wolf Optimization algorithm in terms of super parameter adjustment. The IGWO-ESN neural network is better than all machine learning models mentioned in this paper (GWO-ESN, ESN, BP neural network, least squares support vector machine, and linear regression) in terms of porosity prediction accuracy.