Carbon Management Canada

Distributed Acoustic Sensing (DAS) data acquired in a Vertical Seismic Profile (VSP) configuration is being actively considered as a candidate low-cost monitoring technology for CO2 injection and storage. The University of Calgary “Snowflake” 3D VSP experiment, currently with two surveys spanning 2018-2022, was carried out in part to aid in this assessment. Optimizing 3D time-lapse VSP-DAS data processing and imaging involves several open questions, some of which we address in this study. Our primary focus is on enhancing the quality of upgoing wave data through a comprehensive approach, including phase analysis, denoising, separation of upgoing and downgoing waves, and wavelet characterization. Additionally, to further enhance imaging results, we employ both azimuthally-dependent and reflection angle-dependent reverse time migration (RTM) methodologies. The final imaging results arising from this approach lead to VSP-DAS time-lapse imaging, which may not only be relevant in the CO2 monitoring problem but in a range of applications of this technology.

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

We want to show a framework to solve geophysical inversion problems using a gated based quantum computer. The resultant quantum algorithm belongs to the quantum search algorithm class where the query is a forward modelling calculation, and we look for the minimum residual. This quantum algorithm can find the global minimum in O(pN) queries where N is the number of models. This algorithm does not need gradient or Hessian calculations, only forward modelling, and residuals calculation. We illustrate the algorithm with a small instance of a traveltime tomography problem.

In recent years our research group has observed quasi-transient changes in seismic transmission data associated with CO2 injection (Innanen et al., 2019). Here, we present images generated through elastic full waveform inversion (EFWI) of rapid-repeat time-lapse vertical seismic profile (VSP) field data, acquired with a fixed source shooting across an injection plume at roughly 15-minute intervals over several days. These images, achieved by the combined distributed acoustic sensing (DAS) and geophone data, represent snapshots of clear transient changes in the vicinity of the injection well. The field data inversion images demonstrate the combined sparse-space geophone and DAS VSP can be new and informative monitoring modes.

Seismic data are a significant facilitator for monitoring in carbon capture and sequestration projects, providing high-resolution images of fluid migration, using, for example, full-waveform inversion (FWI). Distributed acoustic sensing (DAS), a relatively novel technology for wavefield sampling, is well suited for this type of monitoring. Using noninvasive optical fibers, DAS allows for dense spatial sampling along the entire length of the wellbore, without disrupting operations. Permanently installed in the wellbore, typically behind casing, DAS offers highly repeatable and dense sampling of the transmitted wave modes crucial to seismic monitoring of injected carbon dioxide ([Formula: see text]). However, the DAS data consist of measurements of strain along the tangent of the fiber and therefore do not transfer directly to conventional FWI algorithms. Incorporation of DAS data in their native strain (or strain-rate) form in standard FWI algorithms, requires changing the definition of the receiver sampling operator to use geometric information about the fiber to supply tangential strain measurements to the FWI residual. The theoretical developments are applied to invert field vertical seismic profile data acquired with DAS fiber and accelerometers at a [Formula: see text] sequestration site in Newell Country, Alberta. Our method incorporates DAS data and accelerometer data in one objective function and allows us to tune the relative importance we wish to place on each data set. This method also transfers to noncollocated sensors, for example, surface-deployed geophones and borehole fiber. The inverted models contain features expected from the geology of the field site, and data modeled in the inverted models compare favorably with the field data for these sensor types. The models are derived from data acquired prior to [Formula: see text] injection, representing baseline models for future time-lapse studies planned at the field research station.

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.

<p class="1Body">Nitrous oxide (N<sub>2</sub>O) emissions from agriculture contributed an estimated 60% of the global total in 2005. In the UK, grassland soils account for 30% of total emissions, 22% of which are estimated to come from urine and dung patches. These patches are possible sources of ‘hot-spots’ (area <em>ca.</em> 1 m<sup>2</sup>) of N<sub>2</sub>O fluxes. Spatial and temporal heterogeneity of N<sub>2</sub>O hot-spot fluxes were investigated in three grassland fields (grazed with dairy cows (DG), grazed with young stock (YG) or cut for silage (SC)) using gas sampling chambers surrounding historic hot-spots to establish their size. Fluxes from old dung and urine patches were measured, as well as freshly applied dung and urine to simulate the creation of hot-spots. Potential chemical and physical drivers were also measured. Large spatial variability of N<sub>2</sub>O fluxes was seen in all three grassland fields. Mean N<sub>2</sub>O fluxes for the historic hot-spots in the grazed fields (DG and YG) were significantly greater than (SC). The mean N<sub>2</sub>O fluxes in DG and YG (117.9 and 243.5 ng N m<sup>-2</sup> s<sup>-1</sup>) were 15 to 30% greater than for SC. Soil temperature (15 - 20 °C) was the most significant driver of N<sub>2</sub>O production with a 1°C rise in soil temperature increasing emissions under DG and YG. N<sub>2</sub>O fluxes were enhanced by the fresh dung but not by urine. However, in the urine treatment, the nutrient input increased the microbial respiration response for the CO<sub>2</sub> flux. Hot-spot N<sub>2</sub>O emissions from old urine and dung patches were persistent several months after application.</p>

This paper outlines the current knowledge of the processes controlling nitrous oxide (N2O) and methane (CH4) fluxes, methods of measurement, mitigation options and models designed to simulate N2O and CH4 fluxes. Natural and managed soils are globally important sources and sinks of the main non- CO2 greenhouse gases (GHG) N2O and CH4. Compared to CO2 their global warming potential over a 100 year period is 298 and 23 times, respectively, larger than that of CO2.