Geological resources and hazards are key issues for socioeconomic development. In areas with significant natural hazard risks, continued overexploitation of the subsurface and increased population growth, understanding the subsurface deformation processes is paramount. Both rocks and cementitious materials in any underground infrastructure are constantly subjected to active stresses. Hence, any natural or anthropogenically induced change in the underground can cause deformation, likely resulting in altering the structure/texture of the materials, the geometry of fluid pathways and thus, the fluid-geomaterial interaction leading to failure.
Wellbores consist a typical example of underground infrastructures that could be affected by such a fluid-geomaterials interaction. For instance, the long-term integrity of existing wells in a CO2-rich environment is essential for ensuring that geological sequestration of CO2 will be an effective technology for mitigating greenhouse gas-induced climate change. The potential for wellbore leakage depends in part on the quality of the original construction as well as geochemical and geomechanical stresses that occur over its life-cycle. Conclusions drawn from field observations have also highlighted how cement-rock interfaces is critical to the long-term performance of wellbores (Crow et al., 2009).
When testing the mechanical integrity of any subsurface material that involves fluid circulation, changes in porosity and permeability values of the tested material are of great interest. At the field-scale porosity and permeability can be defined by a number of geophysical methods (e.g. well logs). At the laboratory-scale these values are either measured using cores (bulk measurements) or are calculated using digital rock models (Van der Land et al., 2013). However, the relationship between porosity and permeability is not always linear but rather depends on the pore structure, the fracture network and connectivity of the pore/fracture networks. Moreover, porosity and permeability values are not uniform throughout the tested materials. These values are influenced by textural changes due to deformation and/or deposition. Thus, a good understanding of the evolution of these petrophysical properties is critical for any field-scale simulation.
Mechanics theory and experiments have demonstrated how brittle deformation results from the growth and coalescence of tiny cracks when the extensional stress threshold is reached. However, the inaccessible location of these deformation features at depth (reservoir-scale) precludes direct observations. Whilst current laboratory results are mostly limited to post-test observations, new techniques have recently allowed for combining direct observation of the deformation under crustal (pressure, stress, fluid and temperature) conditions (e.g. Renard et al. 2016). Moreover, grain-scale observations have brought breakthrough results in terms of the deformation processes, yet upscaling them is not straightforward and a clear connection between the actual deforming state and the parameters measured remains elusive.
Numerical modelling and machine learning techniques are starting to provide answers and fill the gaps (Harrison et al., 2018, for instance). Yet to remain relevant and discover real, previously unknown relationships, it is essential to test the depth and limits of several methods probing the same geophysical properties and enable complementary, synchronous results to be modelled and compared.
To support this methodological evolution, this project aims to jointly investigate the strengths and limits of three non-destructive methods to monitor, image and quantify brittle deformation processes from micro- to decimetre-scale, both in sedimentary rock and cement samples. These geomaterials are present in a wellbore case-study and they will also allow to compare in similar environmental conditions the results obtained in a well-known, controlled material (cement) to a naturally heterogeneous one (sedimentary rock). The experimental conditions will be designed to simulate in-situ reservoir environments to enable knowledge transfer from the laboratory- to the field-scale. The samples will thus be saturated with representative fluid chemistry not only to facilitate the electrical measurements (i.e. to enable current flow between the electrodes) but also for the results to be directly relevant to geological reservoir conditions (Ougier-Simonin et al., 2018).
The samples will first be scanned to reconstruct their internal 3D microstructure, i.e. X-Ray tomography, and initial transport properties (porosity and inferred permeability notably). Several deformation scenario will be investigated and Acoustic Emission (AE) tomography, together with mechanical deformation measurements, will be used to capture in 4D the lab-induced damage associated to brittle deformation. Electrical Impedance Spectroscopy (EIS), together with Electrical Resistivity (ER) tomography, will be combined to obtain the spatial and temporal distributions of the lab-induced damage within the tested materials. The electrical data will be subsequently used to quantify and model the connected porosity and infer on the permeability evolution (Miller et al., 2015). Final X-Ray scans will provide a detailed 3D visualisation of the lab-induced damage, associated with the various parameters monitored and enable new comparative calculation of the porosity and permeability changes.
The objectives of the project are:
O1: Examine the relationship between porosity and permeability jointly via 3 non-destructive experimental methods.
O2: Collect, analyse and model mechanical and geophysical data together.
O3: Develop an experimental methodology to support deep/machine learning on geomaterials (digital rock physics).
O4: Provide relevant tools to better assess brittle deformation so that predictions can be made for incomplete data.
In summary, this PhD project will extend the understanding of brittle deformation processes in geomaterials using an innovative methodology for monitoring and quantifying damage and fluid presence at shallow crustal depths. The combined assessment will also provide an ideal material to refine the emerging machine learning techniques in the rock physics context. Results will be disseminated through peer review journals, conference presentations (e.g. AGU, EGU, ISRM) and workshops (e.g. EAGE). This research will also build new links between geo-resources, civil engineering and natural hazards research communities.
Click on an image to expand
Figure 1: Example of non-destructive methods and digital rock physics. A: X-ray tomography and Acoustic Emission (AE) tomography (each dot is an AE). B: X-ray tomography and electrical properties.
The major strength of the project is the unique combination of 3 non-destructive methods to monitor, quantify and image brittle deformation and fluid presence at depths in geomaterials (i.e., concrete and rocks):
– AE locations are related to the occurrence of different type of microcracks that occur during brittle deformation. This powerful syn-deformation experimental method provides feedback on the spatio-temporal evolution of localised deformation [Ougier-Simonin et al., 2011; Charalampidou et al., 2014; 2015]; the radiated energy is captured by a network of micro-sensors attached around the tested sample.
– EIS and ER: the electrical properties of saturated rocks can be related the formation factor F, rock porosity and cementation exponent (Suryanto et al., 2017). It provides a measure of the pore network (continuity, tortuosity) which is affected by the spatial and extent of damage. The spatial and temporal changes in response can also be used to reconstructed images of the damage process in real time.
– X-Ray tomography is a non-destructive method capturing density variations within the materials. Deformation processes in rocks (Charalampidou et al. 2011; 2014; Renard et al., 2009) induce textural changes (e.g. grain breakage, pore collapse, grain re-arrangement) within the tested samples resulting to local compaction or extension. Comparison of XRT images corresponding to different loading stages (e.g. pre- and post-damage) can be analysed within the frame of Digital Volume Correlation. Such an analysis can provide the 3D displacement and strain maps due to the occurring lab-induced deformation.
Data analysis and modelling will provide the wider frame of knowledge transfer and application.
– Literature review: review and select experimental testing conditions, rock and concrete samples.
– Laboratory training 1/2: sample making and initial characterisation, fluid chemistry and sample treatment, X-ray experiments pre-damage on all samples; EIS and ER tomography experiments on concrete samples.
– Software training (MatLab, InSite, Aviso/Pergeos, EIDORS)
– 1st conference
– Laboratory training 2/2: EIS and ER tomography experiments on concrete and rock samples.
– Data analysis and modelling (1/2), Digital Volume Correlation training
– 1st paper, 2nd conference, workshop
– X-ray experiments post-damage
– Data analysis (image processing, DVC) and modelling (2/2)
– 2nd paper, 3rd conference
– Thesis manuscript
– Fine tuning of models and data interpretation
– 3rd paper
– Thesis manuscript
Departmental training in (a) research skills and techniques and (b) research environment is provided through four mechanisms: (i) a programme of taught modules at HW; (ii) internal training ‘workshops’ that focus on key experimental/laboratory research skills and techniques; (iii) input from supervisors; and (iv) departmental seminars by visiting and internal speakers and presentations by postgraduate students themselves.
The School of Energy, Geoscience, Infrastructure and Society at Heriot Watt University does not require each student to collect a minimum of PGRDP credits (e.g. corresponding to attendance of in-school delivered workshops, taught modules and other activities). However, the student is advised to get a generic training offered by the Heriot-Watt University and/or the School that includes a series of in-house ‘workshops’. Engineering research postgraduates normally take the following Workshops: ‘Essential Skills for Researchers’, ‘ Literature Searching’, ‘Citing and Referencing’, ‘ Critical Thinking’, ‘Strategic Reading of the Research Literature’, Managing Your Research Data’, and ‘ Working with Your Supervisor’ during their first year. The student will benefit from the wide range of taught modules associated with MSc courses in ‘Engineering Geology’, ‘Mapping and Geospatial Data Science’ and ‘Reservoir evaluation and management’. Modules particularly relevant for the project are ‘Geomechanics’, ‘Geomechanics and flow mechanics’, ‘Observation processes and analysis’, ‘Geohazards and Deformation of the Earth’. Most of these modules are delivered in one intensive week so well suited for PhD students.
Research training continues through the second and third years, and is based around a number of themes related to Communicating and Publishing Research: (i) Recognition and validation of problems; (ii) Demonstration of original, independent and critical thinking, and the ability to develop theoretical concepts; (iii) Knowledge of recent advances within the research field and in related areas; (iv) Understanding relevant research methodologies and techniques and their appropriate application within the research field; (v) Ability to analyse and critically evaluate findings and those of others; and (vi) Summarising, documenting, reporting and reflecting on progress.
The student will undergo specialist training in the specific techniques and approaches used in the project. This includes rock and concrete sample preparation for laboratory experiments, design and performance of experimental geophysics tests, acoustic/seismic signal processing on a commercially available software, image analysis processing, manipulation of various data sets and quantitative analysis using Matlab and/or R. The balance between these aspects of the project will depend on the skills, aptitude, and interest of the candidate.
References & further reading
Charalampidou, E. M., Hall, S. A., Stanchits, S., Lewis, M. H. & Viggiani, G., 2011.Characterization of shear and compaction bands in a porous sandstone deformed under triaxial compression. Tectonophysics, 503, 1-2, 8-17.
Charalampidou, E. M., Hall, S. A., Stanchits, S., Viggiani, G. & Lewis, M. H., 2014, Shear-enhanced compaction band identification at the laboratory scale using acoustic and full-field methods. International Journal of Rock Mechanics and Mining Science, 67, 240-252.
Charalampidou, E-M. C., Stanchits, S., Kwiatek, G. & Dresen, G., 2015. Brittle failure and fracture reactivation in sandstone by fluid injection. European Journal of Environmental and Civil Engineering, 19, 5, 564-579.
Crow, W. Williams, B., Carey, J.W., Celia, M., and Gasda, S., 2009. Wellbore integrity analysis of a natural CO2 producer. Energy Procedia: 9th International Conference on Greenhouse Gas Control Technologies, vol. 1, pp. 3561-3569
Harrison, D. G., Efford, N. D., Fisher, Q. J., and Ruddle, R. A., 2018. PETMiner â€” A Visual Analysis Tool for Petrophysical Properties of Core Sample Data, in IEEE Transactions on Visualization and Computer Graphics, 24( 5), 1728-1741, doi:10.1109/TVCG.2017.2682865
Miller, K. J., MontÃ©si, L. G., & Zhu, W. L., 2015. Estimates of olivine-basaltic melt electrical conductivity using a digital rock physics approach, Earth and Planetary Science Letters, 432, 332-341.
Ougier-Simonin, A., Fortin, J., GuÃ©guen, Y., Schubnel, A., & Bouyer, F., 2011. Cracks in glass under triaxial conditions. International Journal of Engineering Science, 49(1), 105-121.
Ougier-Simonin, A., and Pluymakers, A., 2018. Exploring the effect of fluid chemistry on carbonate failure strength, in EGU General Assembly Conference Abstracts, Vol. 20, p19548.
Renard, F., Bernard, D., Desrues, J., & Ougier-Simonin, A., 2009. 3D imaging of fracture propagation using synchrotron X-ray microtomography. Earth and Planetary Science Letters, 286(1-2), 285-291.
Renard, F., Cordonnier, B., Kobchenko, M., and Dysthe, D.K., 2016. 4D X-ray imaging of brittle failure into rock, American Geophysical Union, Fall Meeting 2016, abstract #MR32A-04
Suryanto, B., Saraireh, D., Kim, J. et al., 2017. Imaging water ingress into concrete using electrical resistance tomography, Int J Adv Eng Sci Appl Math, 9, 109-118, https://doi.org/10.1007/s12572-017-0190-9
Van der Land, C., Wood, R., Wu, K., Van Dijke, M.I.J., Jiang, Z., Corbett, P., Couples, G., 2013. Modelling the permeability evolution of carbonate rocks. Marine and Petroleum Geology, Vol. 48, 2013, p. 1-7.