Quantitative CO2 monitoring at the CaMI Field Research Station (CaMI.FRS), Canada, using a hybrid structural-petrophysical joint inversion

2020 ◽  
Author(s):  
Dennis Rippe ◽  
Michael Jordan ◽  
Marie Macquet ◽  
Don Lawton ◽  
Anouar Romdhane ◽  
...  
Keyword(s):  
Joint Inversion ◽  
Field Research ◽  
Research Centre ◽  
Inversion Method ◽  
Model Parameters ◽  
Research Station ◽  
Geophysical Monitoring ◽  
Monitoring Techniques ◽  
Research Institutes ◽  
Structural Joint

<p>A key requirement by the European CCS directive for the safe operation of geological CO<sub>2</sub> storage is the operator's responsibility to demonstrate containment of the injected CO<sub>2</sub> and conformance between its actual and modelled behavior. Understanding the subsurface behavior and long-term fate of the injected CO<sub>2</sub> requires the quantification of key reservoir parameters (e.g. pore pressure, CO<sub>2</sub> saturation and strain in the overburden). Reliable quantification of these parameters and distinction between them pose a challenge for conventional monitoring techniques, which could be overcome by combining advanced multi-disciplinary and multi-method monitoring techniques in a joint inversion.</p><p>Within the <strong>aCQurate</strong> project, we aim to develop a new technology for <strong>a</strong>ccurate <strong>CO<sub>2</sub></strong> monitoring using <strong>Qu</strong>antitative joint inversion for la<strong>r</strong>ge-sc<strong>a</strong>le on-shore and off-shore s<strong>t</strong>orag<strong>e</strong> applications. In previous applications of joint inversion to CO<sub>2</sub> monitoring, we successfully combined the strengths and advantages of different geophysical monitoring techniques (i.e. seismics with its high spatial resolution and geoelectrics with its high sensitivity to changes in CO<sub>2</sub> saturation), using a cross-gradient approach to achieve structural similarity between the different models. While this structural joint inversion provides a robust link between models of different geophysical monitoring techniques, it lacks a quantitative calibration of the model parameters based on valid rock-physics models. This limitation is addressed by extending the previously developed structural joint inversion method into a hybrid structural-petrophysical joint inversion, which allows integration of cross-property relations, e.g. derived from well logs.</p><p>The hybrid structural-petrophysical joint inversion integrates relevant geophysical monitoring techniques in a modular way, including seismic, electric and potential field methods (FWI, CSEM, ERT, MMR and gravity). It is implemented using a Bayes formulation, which allows proper weighting of the different models and data sets, as well as the relevant structural and petrophysical joint inversion constraints during the joint inversion.</p><p>The hybrid joint inversion is designed for on-shore and off-shore CO<sub>2</sub> storage applications and will be demonstrated using synthetic data from the CaMI Field Research Station (CaMI.FRS) in Canada. CaMI.FRS is operated by the Containment and Monitoring Institute (CaMI) of CMC Research Institutes, Inc., and provides an ideal platform for the development and deployment of advanced CO<sub>2</sub> monitoring technologies. CO<sub>2</sub> injection occurs at 300 m depth into the Basal Belly River sandstone formation, which is monitored using a large variety of geophysical and geochemical monitoring techniques. In preparation for the application to real monitoring data, we present the application of the joint inversion to synthetic full waveform inversion (FWI) and electrical resistivity tomography (ERT) data, derived for a geostatic model with dynamic fluid flow simulations.</p><p>In addition to obtaining a better understanding of the subsurface behavior of the injected CO<sub>2</sub> at CaMI.FRS, our goal is to mature the joint inversion technology further towards large-scale CO<sub>2</sub> storage applications, e.g. on the Norwegian Continental Shelf.</p><p><strong>Acknowledgements</strong></p><p>Funding is provided by the Norwegian CLIMIT program (project number 616067), Equinor ASA, CMC Research Institutes, Inc., University of Calgary, Lawrence Berkeley National Laboratory (LBNL), Institut national de la recherche scientifique (INRS), Quad Geometrics Norway AS and GFZ German Research Centre For Geosciences (GFZ).</p>

Joint inversion of Rayleigh-wave dispersion and P-wave refraction data for laterally varying layered models

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. EN49-EN59 ◽  
Author(s):  
Daniele Boiero ◽  
Laura Valentina Socco
Keyword(s):  
Rayleigh Wave ◽  
Joint Inversion ◽  
A Priori ◽  
P Wave ◽  
Inversion Method ◽  
Model Parameters ◽  
Data Set ◽  
Wave Refraction ◽  
Velocity Models ◽  
Refraction Data

We implemented a joint inversion method to build P- and S-wave velocity models from Rayleigh-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments. A priori information available over the site and any physical law to link model parameters can be also incorporated. We tested and applied the algorithm behind the method. The results from a field data set revealed advantages with respect to individual surface-wave analysis (SWA) and body wave tomography (BWT). The algorithm imposed internal consistency for all the model parameters relaxing the required a priori assumptions (i.e., Poisson’s ratio level of confidence in SWA) and the inherent limitations of the two methods (i.e., velocity decreases for BWT).

scholarly journals Constraining the Subsurface Geochemical Baseline of CMC Research Institutes’ Field Research Station (FRS), Alberta

2020 ◽  
Author(s):  
Rachel Utley ◽  
Nicholas Utting ◽  
Gareth Johnson ◽  
Marta Zurakowski ◽  
Domokos Györe ◽  
...  
Keyword(s):  
Field Research ◽  
Research Station ◽  
Geochemical Baseline ◽  
Research Institutes

Joint facies and reservoir properties inversion

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M15-M24 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Convex Combination ◽  
Real Data ◽  
Joint Estimation ◽  
Reservoir Rock ◽  
Inversion Method ◽  
Simultaneous Estimation ◽  
Model Parameters ◽  
Reservoir Properties

I have developed a joint inversion of seismic data for the simultaneous estimation of facies and reservoir properties, such as porosity, mineralogy, and saturation. The inversion method is a Bayesian approach for the joint estimation of facies and reservoir rock/fluid properties based on the statistical assumption of mixtures of nonparametric distributions of the model parameters. A mixture distribution is a convex combination of distributions. In the approach, I use a mixture of [Formula: see text] nonparametric distributions, where [Formula: see text] is the number of facies, and the weights of the combination represent the probability of the facies. The statistical assumptions in the proposed Bayesian inversion allow modeling multimodal and nonsymmetric distributions of the model parameters because they are not restricted to specific shapes of the probability distributions and allow modeling multimodal distributions caused by the presence of different facies and nonlinear relations between reservoir properties and elastic data. The inversion can be applied to elastic properties from well logs or derived from seismic data, and they can be combined with traditional Bayesian linearized AVO inversion. The method provides the point-by-point posterior distribution of facies and reservoir properties, as well as the most-likely models and its associated uncertainty, and it is successfully applied to real data.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

The Research Liaison Committee

1967 ◽  
Vol 10 (02) ◽  
pp. 19
Keyword(s):  
Field Research ◽  
Actual Cost ◽  
Institutional Affiliation ◽  
Academic Advisor ◽  
Liaison Committee ◽  
The Past ◽  
Research Institutes ◽  
Home Institution ◽  
Other Information ◽  
Oriented Research

The Research Liaison Committee of the African Studies Association has compiled A Directory of Studies Centers and Research Institutes Abroad engaged in Africa-oriented research. The Directory is available by individual countries or in its entirety by writing to the RLC office. Professors Igor Kopytoff, Vernon McKay, and Benjamin Rivlin are the 1967 liaison representatives of the Association. Each has visited African universities, research institutes, and government offices during the past few months to collect information on research in progress and on the perspectives and problems of field research in the countries of Africa. The 1968 liaison representatives have been appointed by the Association's president, William A. Hance. Professors Robert A. Lystad and Robert L. West have joined the RLC and will be traveling to Africa during the summer, 1968. A request to scholars recently returned or going to Africa. The RLC would welcome the following information: 1. Data on research project, including title of project, discipline or disciplines reflected, financial sponsorship, home institution, academic advisor, institutional affiliation in Africa, date of departure and expected duration of stay in Africa; 2. A brief report on living conditions, actual cost in relation to anticipated cost, field problems, and any other information with would be of assistance to those planning fieldwork in Africa.

scholarly journals Field Research and Conservation

2013 ◽  
Vol 36 ◽  
pp. 179-180
Author(s):  
Chuck Collis ◽  
Jennifer Adams
Keyword(s):  
Behavioral Ecology ◽  
National Park ◽  
Biological Process ◽  
Field Research ◽  
Research Station ◽  
Surrounding Area ◽  
Research Experiences ◽  
Political Processes ◽  
Term Field

The Field Research & Conservation class emphasizes long-term field research experiences, examines ecosystem processes, and investigates the evolution of American perspectives about nature. Our time spent at the UW-NPS research station was divided between pursuing behavioral ecology research and exploring Grand Teton National Park and the surrounding area to gain understanding of how the region was shaped, both by geological and biological process as well as political processes that have been shaped by America’s ever-changing conservation ethic.

scholarly journals Bayesian rock physics inversion: application to CO2 storage monitoring

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Bastien Dupuy ◽  
Anouar Romdhane ◽  
Pierre-Louis Nordmann ◽  
Peder Eliasson ◽  
Joonsang Park
Keyword(s):  
Joint Inversion ◽  
Co2 Storage ◽  
Rock Physics ◽  
Model Space ◽  
Real Data ◽  
Sensitivity Study ◽  
Model Parameters ◽  
Reservoir Properties ◽  
Upward Migration ◽  
Before And After

Risk assessment of CO2 storage requires the use of geophysical monitoring techniques to quantify changes in selected reservoir properties such as CO2 saturation, pore pressure and porosity. Conformance monitoring and associated decision-making rest upon the quantified properties derived from geophysical data, with uncertainty assessment. A general framework combining seismic and controlled source electromagnetic inversions with rock physics inversion is proposed with fully Bayesian formulations for proper quantification of uncertainty. The Bayesian rock physics inversion rests upon two stages. First, a search stage consists in exploring the model space and deriving models with associated probability density function (PDF). Second, an appraisal or importance sampling stage is used as a "correction" step to ensure that the full model space is explored and that the estimated posterior PDF can be used to derive quantities like marginal probability densities. Both steps are based on the neighbourhood algorithm. The approach does not require any linearization of the rock physics model or assumption about the model parameters distribution. After describing the CO2 storage context, the available data at the Sleipner field before and after CO2 injection (baseline and monitor), and the rock physics models, we perform an extended sensitivity study. We show that prior information is crucial, especially in the monitor case. We demonstrate that joint inversion of seismic and CSEM data is also key to quantify CO2 saturations properly. We finally apply the full inversion strategy to real data from Sleipner. We obtain rock frame moduli, porosity, saturation and patchiness exponent distributions and associated uncertainties along a 1D profile before and after injection. The results are consistent with geology knowledge and reservoir simulations, i.e., that the CO2 saturations are larger under the caprock confirming the CO2 upward migration by buoyancy effect. The estimates of patchiness exponent have a larger uncertainty, suggesting semi-patchy mixing behaviour.

Inversion of Reservoir Parameters by Ultra-High Temperature and High Pressure CO2 Gas Reservoirs with Nuclear Logging

2021 ◽  
Author(s):  
Hengrong Zhang ◽  
Lizhi Xiao ◽  
Wensheng Wu ◽  
Xinyue Fu ◽  
Shenglin He
Keyword(s):  
Joint Inversion ◽  
Simulation Method ◽  
Inversion Method ◽  
Burial Depth ◽  
Gas Reservoirs ◽  
Co2 Gas ◽  
Carbonate Cement ◽  
Response Equation ◽  
Temperature And Pressure ◽  
The Difference

Abstract The Yinggehai basin is located in the western part of the South China Sea, the burial depth of the Huangliu and Meishan formations in the target layer is close to 4000 meters, the formation temperature is close to 200 degrees Celsius, and the formation pressure is up to 100 MPa. The reservoir is characterized by low porosity-ultra-low permeability, heavy carbonate cement, complex CO2 content, this leads to complex neutron and density logging effects. The solubility of CO2 Above CH4, the solubility change with temperature and pressure is different from CH4, which makes it difficult to identify the CO2 gas layer. In this paper, based on the difference in the physical characteristics of CO2 and CH4, the Boltzmann equation combined with MCNP software was used to simulate the neutron and density logging responses under different CO2 saturations. Environmental factors such as temperature and pressure, carbonate cement, mud content and pores were studied To measure the effect of logging response, the LM inversion method is used to jointly invert CO2 saturation of density and neutron logs. The purpose of the inversion is to reduce the non-uniqueness of the evaluation of porosity and CO2 saturation. By introducing the Levenberg-Marquardt (LM) method, the neutron logging response equation of the porosity, argillaceous content, CO2, CH4 in the rock and the corresponding temperature and pressure is solved, and also the response equation of above parameters to density logging, where porosity and CO2 content are the key parameters, and the calculation results prove the effectiveness of the method by comparing the sampling data. The results show that the accuracy of the estimated CO2 saturation is increased by 10% compared with the conventional interpretation method, and the new simulation method improves the calculation speed several times compared to the MCNP software. The joint inversion method has been successfully applied to field data, which has greatly improved the saturation evaluation results of traditional logging interpretation methods, can be extended to other fields of nuclear logging simulation and inversion.

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