Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO₂-saturated rocks. Reliable accounting of CO₂ in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO₂ rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO₂ saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO₂ rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO₂ weakening on the rock framework. We validate our CO₂ rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO₂ rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO₂ accounting using seismic monitoring.

The Farnsworth Unit in northern Texas is a field site for studying geologic carbon storage during enhanced oil recovery (EOR) using CO${}_2$. Microseismic monitoring is essential for risk assessment by detecting fluid leakage and fractures. We analyzed borehole microseismic data acquired during CO${}_2$ injection and migration, including data denoising, event detection, event location, magnitude estimation, moment tensor inversion, and stress field inversion. We detected and located two shallow clusters, which occurred during increasing injection pressure. The two shallow clusters were also featured by large b values and tensile cracking moment tensors that are obtained based on a newly developed moment tensor inversion method using single-borehole data. The inverted stress fields at the two clusters showed large deviations from the regional stress field. The results provide evidence for microseismic responses to CO${}_2$/fluid injection and migration.

We present the current status of time-lapse seismic integration at the Farnsworth (FWU) CO₂ WAG (water-alternating-gas) EOR (Enhanced Oil Recovery) project at Ochiltree County, northwest Texas. As a potential carbon sequestration mechanism, CO₂ WAG projects will be subject to some degree of monitoring and verification, either as a regulatory requirement or to qualify for economic incentives. In order to evaluate the viability of time-lapse seismic as a monitoring method the Southwest Partnership (SWP) has conducted time-lapse seismic monitoring at FWU using the 3D Vertical Seismic Profiling (VSP) method. The efficacy of seismic time-lapse depends on a number of key factors, which vary widely from one application to another. Most important among these are the thermophysical properties of the original fluid in place and the displacing fluid, followed by the petrophysical properties of the rock matrix, which together determine the effective elastic properties of the rock fluid system. We present systematic analysis of fluid thermodynamics and resulting thermophysical properties, petrophysics and rock frame elastic properties, and elastic property modeling through fluid substitution using data collected at FWU. These analyses will be framed in realistic scenarios presented by the FWU CO₂ WAG development. The resulting fluid/rock physics models will be applied to output from the calibrated FWU compositional reservoir simulation model to forward model the time-lapse seismic response. Modeled results are compared with field time-lapse seismic measurements and strategies for numerical model feedback/update are discussed. While mechanical effects are neglected in the work presented here, complementary parallel studies are underway in which laboratory measurements are introduced to introduce stress dependence of matrix elastic moduli.

The criticality of the current climate crisis prompts the International Energy Agency (IEA) to develop greenhouse gas (GHG) emission reduction scenarios to minimize CO2 atmospheric concentrations while ensuring reliable and affordable access to energy and continued economic growth. The 2013 IEA scenario for an 80% chance of limiting global temperature rise to 2 oC by 2050 requires a 14% contribution from Carbon Capture and Storage (CCS). CCS encompasses the capture, transportation, and storage of CO2, while in Carbon Capture Utilization and Storage (CCUS), the captured CO2 is utilized beneficially. A prominent example of CCUS is CO2-EOR flooding. CO2-EOR was first implemented in SACROC West Texas to increase oil recovery and has now also recognized as a valid CO2 sequestration endeavor. In this study, the Farnsworth Field in Texas, USA is a depleted oil reservoir that has gone through primary and secondary recovery. Following that, Water altering CO2 Gas (WAG) injection has been implemented to enhance the oil production further. However, the injection and storage of large volumes of CO2 in the subsurface associates with geomechanical risk increment, and thus, studies concerning the safe, reliable and permanent storage of CO2 containment assurance is necessary. This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. This concept is similar to calibrating and history matching a reservoir simulation model. In that the process, real field data needs to be collected to further improve the model. In our study, the solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. It benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the core samples collected from the injecting formation. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. As a result, four independent and impactful geomechanical parameters have been determined. They are the bulk modulus and shear modulus for zero porosity, and the shear and compressional seismic velocity to the mean effective stress derivatives. The dataset of numerous coupled hydromechanical and geomechanical simulation realizations is built by combining variations of the four stated parameters. Eventually, a machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. The workflow presented in this work will be immensely critical to the security and success of CCS and CCUS projects in any kind.

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

During the Development Phase of the U.S. Southwest Regional Partnership on Carbon Sequestration, supercritical CO₂ was continuously injected into the deep oil-bearing Morrow B formation of the Farnsworth Unit in Texas for Enhanced Oil Recovery (EOR). The project injected approximately 94 kilotons of CO₂ to study geologic carbon storage during CO₂-EOR. A three-dimensional (3D) surface seismic dataset was acquired in 2013 to characterize the subsurface structures of the Farnsworth site. Following this data acquisition, the baseline and three time-lapse three-dimensional three-component (3D-3C) vertical seismic profiling (VSP) data were acquired at a narrower surface area surrounding the CO₂ injection and oil/gas production wells between 2014 and 2017 for monitoring CO₂ injection and migration. With these VSP datasets, we inverted for subsurface velocity models to quantitatively monitor the CO₂ plume within the Morrow B formation. We first built 1D initial P-wave (V_p) and S-wave (V_s) velocity models by upscaling the sonic logs. We improved the deep region of the V_p and V_s models by incorporating the deep part of a migration velocity model derived from the 3D surface seismic data. We improved the shallow region of 3D V_p and V_s models using 3D traveltime tomography of first arrivals of VSP downgoing waves. We further improved the 3D baseline velocity models using elastic-waveform inversion (EWI) of the 3D baseline VSP upgoing data. Our advanced EWI method employs alternative tomographic and conventional gradients and total-variation-based regularization to ensure the high-fidelity updates of the 3D baseline V_p and V_s models. We then sequentially applied our 3D EWI method to the three time-lapse datasets to invert for spatiotemporal changes of V_p and V_s in the reservoir. Our inversion results reveal the volumetric changes of the time-lapse V_p and V_s models and show the evolution of the CO₂ plume from the CO₂ injection well to the oil/gas production wells.