In the past decade, rapid advances in distributed optical fibre sensing technologies have made it possible to record various geophysical data (e.g. strain, temperature and pressure) continuously in both time and space along the fibre, providing an unprecedented quantity and spatial density of data compared to traditional geophysical measurements as well as reducing data acquisition cost. To date, no distributed fibre-based electromagnetic field sensing system has been implemented although electromagnetic sensing could have a broad range of applications to geophysical imaging and monitoring in borehole environments. The goal of this paper is to provide a theoretical feasibility study regarding the design and use of an electromagnetic sensing optical fibre for geophysical applications. First, we present the sensitivity analysis of a ‘hypothetical’ optical fibre coated with polyvinylidene fluoride, a polymer that provides relatively high piezoelectric properties, yet unlike ceramics, is flexible. Using a two-dimensional electromagnetic modelling algorithm, we simulate the earth electric-field-to-fibre-strain transfer function and estimate the theoretical sensitivity of the optical fibre to electric fields. Given the state-of-the-art distributed acoustic sensing strain sensitivities in the picometres strain range, our numerical modelling analysis suggests that a perfectly coupled polyvinylidene fluoride–coated optical fibre can measure electric field values in the mV/m to V/m amplitude range. We then apply a cylindrically symmetric modelling algorithm to simulate numerical models demonstrating the applicability of such a fibre in an oilfield environment. Scenarios investigated employ an electric field source and suggest that the measurements can be used to distinguish the oil versus water ratio with a fibre mounted inside a producing steel cased oil well as well as distinguishing between brine and hydrocarbon filled reservoir zones with a fibre located outside of the casing.

Geologic carbon storage (GCS) is ramping up worldwide as a viable component of carbon capture, utilization, and storage (CCUS) projects aimed at reducing greenhouse pollution to limit climate change. GCS may be a growth opportunity for the application of geophysics in reservoir characterization and monitoring. Federal and state government financial incentives are the economic motivators of the CCUS business in the United States, and recent increases in these incentives have triggered a large number of U.S. Environmental Protection Agency Class VI permit applications to inject CO2 for GCS. The applications indicate that almost all such projects propose using geophysical technology for monitoring. We assessed the GCS geophysical market in the United States based on an intensive analysis of recently filed Class VI permit applications. The analysis shows that reprocessing of existing seismic data will be the primary geophysical activity for reservoir characterization prior to CO2 injection. For monitoring, verification, and recording of CO2 injection, time-lapse vertical seismic profiling and 3D seismic imaging will be the dominant technologies followed by 2D time-lapse seismic imaging and some nonseismic methods. Passive seismic monitoring is planned for the majority of CCUS projects to reduce the risk of induced seismicity. If assumptions related to the United States meeting its current climate goals by 2050 are met, then geophysical activity will increase over the next 30 years. An estimate of the seismic crew count needed to support the projects suggests that the scale of GCS-related seismic acquisition by 2050 may reach the current level of onshore oil and gas geophysics crews in the United States. While the economic incentives of a regulation-driven market will press for the minimization of geophysical sensing in GCS, there is also the potential for growth in geophysical activity with the development of advanced processing and analysis tools, multiphysics data interpretation, and cost-effective continuous monitoring.

Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network's input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.

Reservoir integrity stewardship accompanying carbon capture and sequestration considers fluid extraction and reinjection as a risk-mitigating method against overpressuring that could lead to caprock damage and ensuing CO2 leakage. Crosswell electromagnetics offers a technically viable monitoring method with the spatial volume coverage necessary for reservoir-encompassing pressure management. However, a certain logistic dilemma for deep gas sequestration into saline and thus electrically conductive aquifers is that crosswell magnetic-field measurements underperform in the imaging of more resistive plume bodies, further exacerbated when vertical arrays intersect, as opposed to surround, plumes. Comparative synthetic-data plume imaging of such scenarios rates the information content of magnetic-field versus electric-field 3D crosswell layouts for reservoir and infrastructure conditions of a representative pilot site in a coastal area in Florida. The image quality of the resulting plume replications can be ranked numerically through a newly proposed semblance qualifier, appraising the model goodness of fit to a given reference. In contrast to common least-squares measures for goodness of fit, the semblance formulation uses classifying logistic function types, thus enabling a better distinction of predefined anomaly features.

The Geysers geothermal field located in northern California, USA, is the world's largest electricity-generating geothermal facility. To delineate the spatio-temporal distribution of reservoir steam and recharge water, we have collected microseismic and magnetotelluric (MT) data using a dense array of stations in 2021. The microseismic and MT data have been inverted together using a 3D cooperative joint inversion workflow. The joint inversion exploits a cross-gradient structural constraint because electrical conductivity structures observed in the geothermal field are strongly correlated with VP/VS structures. To mitigate convergence issues associated with 3D large-scale joint inversion, the scheme is split into small manageable inversion subsets. By systematically performing the three inversion subsets and exchanging the structural information between velocity and conductivity models, the cooperative joint inversion mimics the full joint inversion. The 3D joint inversion results agree well with previous 3D microseismic and MT inversion studies. We find that the cooperative joint inversion improves overall MT images in terms of resolution and consistency with respect to the VP/VS model. The joint inversion also further reduces the MT data misfit. In contrast, the joint inversion does not significantly improve microseismic images because MT inversion produces low-resolution conductivity images with respect to microseismic images in depth of investigation (i.e., 1-5 km) and thus does not provide sufficient structural details that are required for improving microseismic images.

In geophysical inversions, lower and upper model parameter bounds are a means of solution stabilization. Further, constraints that intend to let only geologically plausible inverse solutions pass are amenable to lower and upper bounds. Reliable prior information is paramount to construct such bound constraints. It is common practice to narrow and widen bound intervals for regions of, respectively, more and less certain prior information. Contrary to this practice, we experiment with widened bound intervals in zones that are poorly resolved by a given survey configuration but where prior information would suggest structural anomalies of interest. The purpose of enlarged parameter bounds that correlate spatially with predefined targets is to let the inversion explore a larger solution space, thus increasing the potential to resolve otherwise hidden anomalies. Application of the method is based on a carbon-sequestration baseline (pre-injection) crosswell electromagnetic (EM) field survey at the Containment and Monitoring Institute Field Research Station (Alberta, Canada), where impeded measurements led to generally reduced sensitivities for the interwell region. Synthetic-data proofs of concept use augmented bounds designed to boost the resolution of artificial plume targets, indicating an enhanced illumination compared to constant bounds. Comparative field data inversions with spatially variable bounds constructed from prior resistivity and velocity information highlight non-horizontal baseline structures.