DAS field dataset to compare technologies and deployment scenarios

This report describes a Distributed Acoustic Sensor (DAS) dataset acquired by DigiMon partners at the Containment and Monitoring Institute’s (CaMI) Field Research Station (FRS), Canada, between 6th to 10th September 2021. The field dataset contributes to the Deliverable D1.1 of the DigiMon project (DAS field dataset to compare technologies and deployment scenarios), which supports tasks 1.2 and 1.3 of the project. The objective of the DigiMon project is to develop an early-warning system for Carbon Capture and Storage (CCS), which utilises a broad range of sensor technologies including DAS. While the system is primarily focused on CCS projects located in shallow offshore environment of the North Sea, it is also intended to be adaptable to onshore settings. Some of the key areas that the systems will monitor include the movement of the plume within the reservoir, well integrity, and CO2 leakage into the overburden. A combination of both active and passive seismic methods will be deployed to track the movement of CO2, for example seismic reflection to image seismic velocity changes and microseismics to capture fault activation. Acquiring seismic surveys using DAS is highly novel and offers cost-effective approach which can significantly increase the spatial resolution of the survey data; however, it has had limited use in the operational environment with several technical challenges still needing to be resolved, such as the transfer function of DAS. CaMi FRS was selected as a field test location as the site has been specifically established to advance the development of monitoring technologies and protocols for CCS operations. At CaMi FRS, several different monitoring arrays have been installed which are directly applicable to DigiMon. This includes a 5km loop of DAS optical fibre, located with a 1.1 km surface trench and two observation wells, an array of surface and borehole geophone nodes, and 6 broadband seismometers operating by the University of Bristol. This monitoring infrastructure has been primarily installed to monitor CO2 injections into the Basal Belly River sandstone formation at approximately 300m below ground level. Injection of CO2 began at FRS in 2019 and during this time microseismic events have been recorded, albeit at shallower levels than the injection point. The site therefore provides a potential DAS dataset which contains both active and passive measurements for the DigiMon project. The abundance of instrumentation including DAS, geophones, and broadband seismometers provides a unique chance to test the capacity of these instruments for C02 storage monitoring

Project report and algorithms for integrated inversion of individual DigiMon data components

Different data types carry different information about the subsurface, so there should be advantages in combining information from different data types when seeking to infer subsurface properties such as changes in CO2 saturation and pressure with time. We have considered the following data types: conventional seismic data; gravimetric data, and; distributed acoustic sensors (DAS) data. These data types, and the corresponding forward-modelling techniques, are described in Vandeweijer et al., 2021, Bhakta et al., 2023. An important aim for the DigiMon project is to qualify a cost-efficient monitoring system for use with large-scale CO2 sequestration. It is therefore of particular interest to assess if it is possible to obtain satisfactory monitoring results without using the most acquisition-expensive data type(s). Acquisition of conventional seismic data is considerably more costly than acquisition of gravimetric and DAS data combined. In addition to comparing the monitoring performances of the individual data types, we have therefore also compared the performance of gravimetric and DAS data combined, to that of conventional seismic data. We have developed a modelling framework for geophysical monitoring with the abovementioned geophysical data types that in addition to a best estimate of the monitoring target also quantifies the uncertainty in that estimate. The framework uses an ensemble-based implementation of Bayesian (and sequential Bayesian) statistics to achieve this at an affordable computational cost for the numerical examples studied. If the correct monitoring results are known, which they will be if a study with synthetic data is conducted, we can therefore assess with what certainty a particular data type produced better results than another data type for the study example in question

Framework for forward modelling of the DigiMon data

Deliverable D2.1 adds to the main goal of WP2 of the ACT DigiMon project, which is to develop the integrated DigiMon system. The key target for WP2 is to optimally integrate various system components into a reliable and usable system. This deliverable (D2.1) describes the key forward modelling tools of the DigiMon monitoring system. In particular, the modelling tools required to simulate the data response for the individual DigiMon system components that is; Distributed Acoustic Sensing (DAS), conventional seismic, 4D gravity data, and seafloor deformation.Framework for forward modelling of the DigiMon datapublishedVersio

DAS synthetic dataset

Deliverable D1.3 of the ACT DigiMon project is a synthetic microseismic distributed acoustic sensins (DAS) dataset. There are a number of possible uses for such a dataset; for example supporting the development and testing of DAS processing algorithms, testing the efficacy of different array geometries in detecting and characterising events, or simulating a field experiment to better understand observed processes. Given the large number of possible uses it was decided that rather than simply delivering a collection of files of synthetic seismic events, it would be more valuable to deliver a modelling framework from which synthetic data can be generated as the need arises, combined with a small example dataset of a few events to demonstrate the capabilities. DAS systems record seismic wavefields and ground motion due to their sensitivity to strain along the axis of the fibre. To understand the response of DAS it is necessary to understand (1) the seismic source, (2) the path effects and (3) the site and instrument effects. In this report we discuss the modelling of the first two contributions of the DAS response; the source and path effects. We simulate the resulting particle motion and strain at the fibre location, resulting from realistic microseismic sources in geological models representative of the North Sea. The third contribution; site and instrument effects, is contained in the transfer function, which describes the mathematical relationship between the wavefield properties at the cable location to the recorded DAS output. The form of the transfer function is a key unanswered question which will be addressed in Task 1.2 of the DigiMon project

TRA of DigiMon components

The DigiMon project aims to develop an affordable, flexible, societally embedded and smart monitoring system for industrial scale subsurface CO2 storage. For this purpose, the DigiMon system is to combine various types of measurements in integrated workflows. In this report, we describe the process of conducting the Technology Readiness Assessment (TRA) of various measurement techniques. We report on the identification, description and assessment of these measurement techniques as Critical Technology Elements (CTEs) being part of the DigiMon system

DAS Processing Workflow

This report addresses deliverable D1.6 of the DigiMon project, which covers the processing workflow for datasets acquired by Distributed Acoustic Systems (DAS), and follows on from the DAS Preprocessing workflow report (DigiMon Deliverable 1.4), which captured the key stages required to prepare the raw seismic data for the main processing stages. The workflows are specifically for microseismic and ambient noise interferometry methods, which are both passive seismic methods that seek to image CO2 movement within a storage reservoir and potential breaches of the reservoir

WP2 final report

This document summarises the significant results in work package 2 of the DigiMon project. Detailed descriptions and results from each task can be found in the referenced deliverables and publications

Critical technology elements (WP1)

The overall objective of the DigiMon project is to “accelerate the implementation of CCS by developing and demonstrating an affordable, flexible, societally embedded and smart Digital Monitoring early warning system”, for monitoring any CO2 storage reservoir and subsurface barrier system. Within the project the objective of WP1 was to develop individual technologies, data acquisition, analysis techniques and workflows in preparation for inclusion in the DigiMon system. The technologies and data processing techniques developed as part of WP1 include distributed fibre-optic sensing (DFOS) for seismic surveys and chemical sensing, 4D gravity and seafloor deformation measurements, a new seismic source and seismic monitoring survey design. For these technologies the key targets for WP1 were • Develop individual components of the system to raise individual technology readiness levels (TRLs), • Validate and optimise processing software for individual system components, • Develop an effective Distributed Acoustic Sensing (DAS) data interpretation workflow. This work was performed with the expected outcomes of • Raising the DAS TRL for passive seismic monitoring, • An assessment the feasibility of using Distributed Chemical Sensing (DCS) for CO2 detection, • Reducing the cost of 4D gravity and seafloor deformation measurements

Governance of shallow geothermal energy resources

Successful electrification of cities' heating and cooling demands depends on the sustainable implementation of highly efficient ground source heat pumps (GSHP). During the last decade, the use of shallow geothermal energy (SGE) resources in urban areas has experienced an unprecedented boost which nowadays is still showing a steady 9% market growth trend. However, the intensive market incorporation experienced by this technology entails different responsibilities towards the long-term technical and environmental sustainability in order to maintain this positive trend. Here we present a SGE management framework structure and a governance model agreed among 13 European Geological Surveys, providing a roadmap for the different levels of management development, adaptable to any urban scale, and independent of the hydrogeological conditions and the grade of development of SGE technology implementation. The management approach reported is based on the adaptive management concept, thus offering a working flow for the non-linear relationship between planning, implementation and control that establishes a cyclical and iterative management process. The generalized structure of the SGE management framework provided allows the effective analysis of policy to identify and plan for management problems and to select the best management objectives, strategies and measures according to the policy principles proposed here