Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs

In the petroleum industry, artificial intelligence has been applied in seismic and logging interpretation, accurate modeling, optimized drilling operations, well dynamics prediction, safety warning, etc. However, field-scale application deployment remain a challenge due to lack of sufficiently powerful algorithms for integration multi-granularity data time space domain, construction deep-learning network able represent evolution reservoir dynamics, finally investment surveillance acquisition. This paper offers concise review smart field mature waterflood reservoirs, including current status foundation construction, research progress AI algorithms, as scenarios overall deployment. With respect data, domestic international oil gas industry completed or at least started large-scale lake data. isolation phenomenon is serious China. Preparation new monitoring reservoirs insufficient. data-based model-based have emerging recently, but development proxy model rapid prediction automatic calibration still preliminary period. For scenarios, relatively simple independent applications related geophysical interpretation production engineering are continuing emerge, while require substantial acquisition, game-changing with cloud-based computing architecture, top-down