Application of Inter-Well Connectivity Analysis with a Data-Driven Method in the SAGD Development of Heavy Oil Reservoirs

The development of heavy oil reservoirs in China is of great significance to safeguard national energy security, but great challenges are faced due to the complex and heterogeneous reservoir properties. Inter-well connectivity analysis is critical to enhancing the development performance, as it is a good way to interpret fluid flow and provides a theoretical basis for injection-production optimization. Data-driven deep learning methods have been widely used in reservoir development and can be employed to develop surrogate models of injection and production and to infer inter-well connectivity. In this study, the model performance of a recurrent neural network (RNN) and its four variants were evaluated and compared in a temporal production prediction. The comparison results showed that bidirectional gated recurrent unit (Bi-GRU) is the optimal algorithm with the highest accuracy of 0.94. A surrogate model was established to simulate the inter-well connectivity of steam-assisted gravity drainage (SAGD) in the research area by utilizing the Bi-GRU algorithm. A global sensitivity analysis method, Fourier amplitude sensitivity testing (FAST), was introduced and combined with the surrogate model to explain the influence of the input variables on the output variables by quantitatively calculating the sensitivity of each variable. Quantitative results for the inter-well connectivity of SAGD were derived from the sensitivity analysis of the proposed method, which was effectively applied to typical linear patterns and five-spot patterns. Inter-well connectivity varied from 0.1 to 0.58 in test applications, and mutual corroboration with previous geological knowledge can further determine the distribution of the interlayer in the reservoir. The workflow proposed in this study provides a new direction for analyzing and inferring the inter-well connectivity of SAGD in Northeast China heavy oil reservoirs.