Reservoir Lithology And Fluid Prediction From Seismic Data Based On ConvLSTM Neural Network

The economic viability of a field depends on the quality and accuracy of predictions of lithology and fluid distribution,as well as the heterogeneity of the underlying reservoir.Accurate identification of lithology and fluids is the key to successful oil and gas exploration and production.The rise of unconventional resource exploration and the increasing complexity of conventional blocks have made accurate lithology and fluid predictions even more critical.However,the traditional methods of lithology and fluid identification have problems such as relying on manual work,strong subjectivity,and poor flexibility.In order to improve the accuracy and efficiency of lithology and fluid identification,this thesis studies a lithologic fluid identification method based on Conv LSTM neural network.Considering that the deposition of the stratum is temporally gradual,and the lithology and fluid are the response of the stratum depositional characteristics,with certain temporal characteristics,the improved Conv LSTM neural network based on LSTM neural network is used to identify the lithologic fluid.The main work flow is as follows: First,preprocess logging data and reconstruct logging data;Then perform seismic elastic parameter inversion and sensitivity analysis;Construct the network training samples with high parameters;Finally,input the actual seismic data into the neural network to obtain the predicted profile and analyze the results.The test results show that using NVIDIA RTX2070 operation,the training process takes about 1 hour,and the accuracy of lithology and fluid reaches 90.2% and 90.3%,respectively.It shows that the Conv LSTM neural network can realize the automatic identification of the lithology and fluid of the seismic section,and the prediction accuracy and efficiency also reach the standard of actual production.