A Novel Deep Reinforcement Sensor Placement Method for Waterfront Tracking

Abstract

Abstract Waterfront movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the waterfront movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new deep reinforcement learning algorithm for the optimization of sensor placement for waterfront movement detection in carbonate fracture channels. The framework deploys deep reinforcement learning approach for optimizing the location of sensors within the fracture channels to enhance waterfront tracking. The approach first deploys the deep learning algorithm for determining the water saturation levels within the fractures based on the sensor data.. Then, it updates the sensor locations in order to optimize the reservoir coverage. We test the deep reinforcement learning framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, around 5 mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fractures in conventional and unconventional reservoirs [1]. It establish a wireless network connectivity via magnetic induction (MI)-based communication since it exhibits highly reliable and constant channel conditions with sufficient communication range in the oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fractures that record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation to improve sensor data quality, as well as better track the penetrating waterfronts. They will move with the injected fluids and distribute themselves in the fractures where they start sensing the surrounding environment's conditions and communicate data, including their location coordinates, among each other to finally send the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network will be transmitted to the control room via aboveground gateway for further processing. The results exhibited resilient performance in updating the sensor placement to capture the penetrating waterfronts in the formation. The framework performs well particularly when the distance between the sensors is sufficient to avoid measurement interference. The framework demonstrates the criticality of adequate sensor placement in the reservoir formation for accurate waterfront tracking. Also, it shows that itis a viable solution to optimize sensor placement for reservoir monitoring. This novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system for carbonate reservoirs. The results outline the opportunity to deploy advanced artificial intelligence algorithms, such as deep reinforcement methods, to optimally place downhole sensors to achieve best measurement success, and track the waterfronts as well as determine sweep efficiency.