Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production.

The oil and gas industry employs numerical simulation tools extensively in reservoir analysis and strategic planning. This study presents a machine-learning proxy model, employing a Few-shot Learning approach with a Deep Convolutional Generative Adversarial Network (DC-GAN) to reduce computational costs using fewer training samples. The DC-GAN generates new training samples by synthesizing spatial attributes, reservoir parameters, and time series data, thus enhancing the sample variability and diversity needed for accurate production prediction. Also, the study proposes a straightforward and efficient method for data augmentation that primarily involves replicating the initial training dataset. The accumulated production forecast generated from geostatistical realizations enables intelligent reservoir management through risk analysis. The technique can reduce the processing footprint by 70%. Unlike most reservoir studies that employ synthetic datasets, this investigation adopts a real, high-dimensional, and complex reservoir model. [ABSTRACT FROM AUTHOR]