A Data-Efficient Surrogate Model via Simplified Feature Extraction and Pre-Training for Automatic History Matching

Automatic history matching traditionally depends on a large number of time-consuming numerical simulations, which makes the overall workflow computationally expensive. Deep learning-based surrogate models provide an efficient alternative, but their predictive performance often relies on large labeled datasets, whose generation through reservoir simulation remains costly. To alleviate this issue, we propose a data-efficient surrogate modeling framework for automatic history matching. The framework consists of two components. First, the reservoir parameter field is reformulated as a flattened representation and processed using one-dimensional convolutions. This representation provides a direct connection between parameter encoding and production-sequence prediction while maintaining competitive forecasting accuracy. Second, an autoencoder is pre-trained on unlabeled parameter realizations, and the learned encoder is then used to initialize the surrogate model for supervised regression, thereby improving the utilization of inexpensive, unlabeled data. The proposed framework is evaluated on the three-dimensional Brugge benchmark reservoir model. Results show that the one-dimensional representation achieves competitive predictive accuracy with shorter training time. In addition, the pre-training strategy is particularly beneficial when labeled simulation data are limited. Overall, the proposed framework improves the data efficiency of surrogate-assisted automatic history matching and reduces the dependence on extensive labeled simulations in the Brugge benchmark.