Teverra LLC — Department of Energy SBIR Phase I: 20a

Description

Over the past two decades, there has been a drastic advance in the sensing technologies and data acquisition systems. These technologies have been deployed in carbon sequestration to continuously monitor CO2 migration and location during and after injection. A complex network of surface and underground sensors is often installed generating large volume of intricate datasets. These datasets require swift, automated and affordable interpretation and storage to enable both real-time decision-making and post-analysis. However, the conventional data analysis and interpretation mechanisms are often rudimentary, requiring significant time to transmit the high- resolution data to the cloud, and the processing needs significant human intervention. Furthermore, the hardware platform is usually designed and implemented for data acquisition and sampling, and the microcontroller architecture cannot provide any computational power to carry out the analytics at the sensor node. Therefore, the entire process is curbed by human aptitude, computational power, and the available bandwidth. Monitoring, Verification, and Accounting (MVA) to confirm permanent storage of CO2 in geological formations is a significant cost component of any carbon storage campaign and indeed, necessary for its success. Automated and low-cost MVA solutions can advance Carbon Capture and Storage (CCS) towards commercialization by providing a reliable and real-time control option over the reservoir as well as reducing the associated costs. Such solutions should of course address the data management’s bottlenecks available today including but not limited to insufficient bandwidth, inadequate storage and limited connectivity. A key solution to overcome these challenges is to reduce the volume of the recorded data streamed on site. This is achieved by developing adaptive data acquisition techniques to enable data compression at sensor location. Such techniques must afford optimal computational complexity to allow for implementation on conventional microcontroller’s structure at the senor (surface or downhole), sensor node, or the base station. This research aims in studying the feasibility of designing and developing an automated data collection system to be integrated with various types of surface and subsurface sensors and data acquisition systems used for CO2 monitoring. The system software incorporates innovative data compression techniques such as Piecewise Linear Approximation (PLA) and Asymmetric Non-uniform Compression with the goal of adaptive data acquisition and smart segmentation to address the issues of low latency, inadequate bandwidth, and limited storage. The system’s hardware consists of a low-cost solar powered edge computing platform, in particular, Raspberry Pi Compute Module (or a cluster of modules), tailored to the complexity of our proposed data compression algorithms, affordable for performing data analytics on the edge, and scalable to diverse hardware scenarios on site. The ultimate goal of this solution is enabling efficient collection of data while enhancing the extraction of meaningful information, and hence empowering better decision-making in real-time for monitoring CO2 reservoirs.