DAS dataset suitable for microseismic and ANI analysis

Deliverable 1.2 concerns a DAS dataset suitable for microseismic and ambient noise interferometry (ANI). For this deliverable the DAS field dataset of FORGE is recommended. FORGE is the Frontier Organization For Research in Geothermal Energy, and is a field laboratory for developing an enhanced geothermal system in hot crystalline rock situated near the town of Milford in Utah, USA (https://utahforge.com/). The FORGE team is led by Joe Moore of Utah (and funded by the US Department of Energy) and is credited for this dataset. The dataset is completely open access, but obviously attribution would be appreciated in any publications. The FORGE dataset applies for deliverable 1.2, because it provides downhole DAS and geophone recordings of microseismic events, and covers approximately two weeks of continuous DAS recordings that can be used to test the potential of DAS for the ANI method. In addition to the FORGE dataset, various other DAS datasets have recently become publicly available that are recommended to consider as well for further work in task 1.3 and associated tasks, since they can be valuable in addressing different research aspects of the application of DAS. Table 1.1 gives a summary of the different open access datasets considered for this deliverable. This table also shows whether the datasets are suitable to be used for microseismic and ANI analysis. With this application in mind for deliverable 1.2, and when compared against alternative datasets (see Table 1.1), the FORGE dataset is considered to be especially relevant for this deliverable, since it provides both microseismic event data and continuous DAS recordings from a borehole configuration spanning a relatively long duration (17 days). The borehole configuration is preferable for the purpose of detecting micro-seismicity since it allows measurements close to the reservoir and therefore able to detect weaker events compared to a trenched deployment at the surface. FORGE concerns an enhanced geothermal system and in this setting the mechanism driving seismicity is different compared to the case of CO2 injection and storage (DIGIMON). However, the performance of the DAS cable with respect to detected seismicity is expected to be similar for the case of monitoring CO2 injection and storage as in a geothermal setting and therefore the FORGE dataset is expected to be suited for this purpose

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

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

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

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

Project report on WP1 outcomes relevant to other WPs

This report summaries some of the key technologies that have been studied and developed through WP1 with the purpose of transferring these finding to other WPs in the DigiMon 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 Distributed Acoustic Sensing (DAS). While the system is primarily focused on the CCS projects located in the 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 different methods will be adopted to monitor these key areas, which include active and passive seismics, gravimetry, temperature and chemical sensing. This report focuses on technology and methods which have been developed by the DigiMon project and is not intended as a technology review, which is instead the focus of the DigiMon deliverable 2.3 Technology Readiness Assessment

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

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