Generative Adversarial Networks Based Forward-Inverse Method for Geophysical Logging
Authors:   China University of Petroleum, Shao Rongbo , Xiao LiZhi ,   China University of Petroleum, Liao GuangZhi ,   China University of Petroleum, Luo Sihui ,   China University of Petroleum, Luo Gang ,   China University of Petroleum, Zhou Jun ,   China University of Petroleum, Zhou Jun ,   PetroChina Logging Co., Ltd, Li Guojun ,   PetroChina Logging Co., Ltd
Published: November 2024

Forward and inverse models are the central and common problems in well-logging evaluation. Logging inverse problems, however, are ill-posed, pathological, and uncertain, which are caused by the inverse problem itself and are a difficult problem to be faced in the interpretation of logging data. Manual log interpretation relies solely on geophysical mechanics and logging data to understand downhole conditions, which are complex and variable. The subjectivity of the interpretation expert can have a significant impact on the interpretation results, and re-analysis of the data set using new data analysis and processing tools, such as machine learning, could lead to significant discoveries. For data-driven supervised learning models, the quality and quantity of the data set directly determine the quality of the model, and the construction of the data set is also the most time-consuming step in machine-learning modeling. However, there are some problems in geophysical logging data sets, such as small samples and few labels, which seriously restrict the development of machine learning. Considering the importance of data to the development of logging AI technology and the outstanding performance of the generative adversarial network (GAN) model in data generation, we propose to use the GAN model to generate logging data according to the prior information and evaluate formation with logs. The generator (G) is a forward model, which inputs random noise z and virtual formation representations y’, and outputs conventional logging curves X’=G(z,y’). The predictor (P) and discriminator (D) are inversion models. In the dscriminator construction, we use the advantages of the CNN model in two-dimensional data processing and analyze the data from two aspects of depth and feature. The predictor inputs logging data and outputs predicted formation representations y*=P(X), in which construction, we use the advantages of Bi-LSTM in sequence data analysis for formation representations prediction. The discriminator inputs the logging data and outputs the probability that the curve is true. In the model training process, the forward and inverse models are fixed in turn, and the two are trained alternately to continuously improve the model performance and finally reach Nash equilibrium. Compared with traditional GAN, we added a predictor to predict formation representations and input the formation representations to the generator to control logging data generation. We make use of the game method of the GAN, combining the forward and inverse models. Experiment results show that with a small amount of labeled logging data, we could get a forward model capable of generating logging data from given virtual formation representations, and the forward generation data distribution matches the measured data distribution. In terms of logging reservoir parameters prediction, it can effectively overcome the obstacles of small samples and few labels on model training and get a logging intelligent inversion model with ideal performance. This method has specific practical value for intelligent oil-gas exploration to build machine-learning forward-inversion models for intelligent reservoir evaluation and logs data generation with small sample data.