An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging Using Distributed Pressure and Temperature Measurements

Abstract

Abstract Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emission. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life-cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of the high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning-based workflow that can efficiently visualize CO2 plume in near real-time. Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure and temperature as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: first, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower dimensional latent variables that compress high dimensional output images. The ‘onset time’ is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning-based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real-time. The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning-based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning-based framework. The novelty of this work is the development and applications of a unique and efficient deep learning-based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.