Geology-Constrained Time Series Generative Adversarial Network for Well Log Curve Reconstruction

The anomalous logging responses caused by complex geological and downhole engineering conditions, which can be the expansion of a borehole, the formation of fractures, and the mud intrusion, usually result in the absence of some important curves and undermine the accuracy of the reservoir evaluation. The strong nonlinearity and non-stationarity of the log curves remain problematic to conventional interpolation and statistical techniques; the traditional models do not take into account any sequential relationship between points along the depth axis, whereas the deep sequence models can only regress on the points, which limits their capability of ensuring the overall geological consistency. In order to resolve these difficulties, this paper introduces a Geology-Balanced Time Series Conditional Generative Adversarial Network (GC-TSGAN) in which the lithological data is converted into an initial state in the form of prior conditions and is input into both the generator and the discriminator. The model uses LSTM to learn depth-sequential dependencies and a BCE GAN-based adversarial loss to achieve distributional consistency and local morphological fidelity. Hyperparameter tuning is used with the help of random search and Bayesian optimization. The logging data of 41 wells in the B Basin, Chad, are experimented using GC-TSGAN alongside baseline models such as RF, XGBoost, LSTM and ANN; GC-TSGAN is proven to be much better than baseline models in terms of the RMSE, MAE, and squares of predicate and value. The findings confirm that the proposed model can effectively reconstruct log curves with high precision even in a complicated geological environment, thereby providing quality data for performing geological modeling and evaluating the reservoirs.