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

Waterflooding is the main development technique used in the vast majority of oilfields in the world. However, as the waterflood oilfields enter the stages with (ultra-)high water cut, the water/power consumption per ton of oil remains unabatedly high. This paper presents a systematic analysis of the development stages and strategies of some typical waterflood oilfields in the world and proposes the necessity to establish a mode and technical system of collaboration between oil development and water/power consumption in mature oilfields with (ultra-)high water cut. Then, taking a pilot test area of an ultra-high-water-cut oilfield in northeastern China as an example, this paper investigates its development mode and water/power consumption; optimizes the technical strategies from the aspects of fine reservoir analysis, artificial lift system and surface gathering system; and predicts the application results. It is found that the optimizations of fine reservoir analysis and development plans effectively reduce the water consumption per ton of oil and have a certain but not remarkable contribution to the power consumption per ton of oil. Moreover, the optimizations of the artificial lift system and surface gathering system significantly reduce the power consumption per ton of oil. Under conditions of low production and low efficiency, the optimization of the artificial lift system greatly improves the energy-saving effect, which will be a focus of future research. Finally, this paper recommends measures for collaboration between oil development and water/power consumption in high-water-cut oilfields.