Subsurface Characterization Research Articles

Challenges in shallow target reconstruction by 3D elastic full-waveform inversion — Which initial model?

Elastic full-waveform inversion (FWI) is a powerful tool for high-resolution subsurface multiparameter characterization. However, 3D FWI applied to land data for near-surface applications is particularly challenging because the seismograms are dominated by highly energetic, dispersive, and complex-scattered surface waves (SWs). In these conditions, a successful deterministic FWI scheme requires an accurate initial model. Our study, primarily focused on field data analysis for 3D applications, aims at enhancing the resolution in the imaging of complex shallow targets, by integrating devoted SW analysis techniques with a 3D spectral-element-based elastic FWI. From dispersion curves, extracted from seismic data recorded over a sharp-interface shallow target, we build different initial S-wave ([Formula: see text]) and P-wave ([Formula: see text]) velocity models (laterally homogeneous and laterally variable), using a specific data transform. Starting from these models, we carry out 3D FWI tests on synthetic and field data, using a relatively straightforward inversion scheme. The field data processing before FWI consists of band-pass filtering and muting of noisy traces. During FWI, a weighting function is applied to the far-offset traces. We test 2D and 3D acquisition layouts, with different positions of the sources and variable offsets. The 3D FWI workflow enriches the overall content of the initial models, allowing a reliable reconstruction of the shallow target, especially when using laterally variable initial models. Moreover, a 3D acquisition layout guarantees a better reconstruction of the target’s shape and lateral extension. In addition, the integration of model-oriented (preliminary monoparametric FWI) and data-oriented (time windowing) strategies into the main optimization scheme has produced further improvement of the FWI results.

3D Subsurface Characterization of Banded Iron Formation Mineralization using Large-Scale Gravity Data: A Case Study in Parts of Bharatpur, Dausa and Karauli Districts of Rajasthan, India

3D Subsurface Characterization of Banded Iron Formation Mineralization using Large-Scale Gravity Data: A Case Study in Parts of Bharatpur, Dausa and Karauli Districts of Rajasthan, India

3-D seismic interpretation of stratigraphic and structural features in the Upper Jurassic to Lower Cretaceous sequence of the Gullfaks Field, Norwegian North Sea: A case study of reservoir development

The 3-D seismic dataset is a key tool to analyze and understand the mechanism of structural and stratigraphic hydrocarbon (HC) trapping in the subsurface. Conventionally used subsurface seismic characterization methods for fractures are based on the theory of effective anisotropy medium. The aim of this work is to improve the structural images with dense sampling of 3-D survey to evaluate structural and stratigraphic models for reservoir development to predict reservoir quality. The present study of the Gullfaks Field, located in the Norwegian North Sea Gullfaks sector, identifies the shallowest structural elements. The steepness of westward structural dip decreases eastward during the Upper Jurassic to Lower Cretaceous deposition. Reservoir sands consist of the Middle Jurassic deltaic deposits and Lower Jurassic fluvial channel and delta plain deposits. Sediment supply steadily prevails on sea-level rise and the succession displays a regressive trend indicated by a good continuous stacking pattern. The key factor for the development of reservoirs in the Gullfaks Field is fault transmissibility with spatially distributed pressure. The majority of mapped faults with sand-to-sand contacts are non-sealing, which provide restriction for the HC flow between the fault blocks. The traps for HC accumulation occur between the post-rift and syn-rift strata, i.e. antiform set by extensional system, unconformity trap at the top of syn-deposition, and structural trap due to normal faults. Overall reservoir quality in the studied area is generally excellent with average 35% porosity and permeability in the Darcy range. Our findings are useful to better understand the development of siliciclastic reservoirs in similar geological settings worldwide.

Resolution of non-destructive imaging by controlled acceleration voltage in scanning electron microscopy

A new method of non-destructive sub-surface interfacial characterisation has been developed recently, which can be useful for quality assurance of a buried interface in nanoelectronic devices, such as magnetic random access memory. Since the cell size of these devices have been reducing their sizes, it is important to evaluate the resolution of the non-destructive imaging. A sub nanometric layer of different materials such as W and Pt was grown underneath a capping layer with controlled thickness for the evaluation of their sizes in this study. This provides systematic experimental data to show that the technique is capable to resolve down to approximately 2 nm in the plane, which is sufficient for the device imaging.

Integrated processing and interpretation to image and derisk a carbonate reservoir clouded by shallow gas — A case study from offshore Vietnam

Carbonate reservoirs are increasingly becoming an important resource for hydrocarbon production because they contain the majority of remaining proven oil and gas reserves. In this context, carbonate reservoirs could represent new opportunities; however, there is still a lack of understanding of their subsurface status and characterization. Carbonate reservoirs are more difficult to evaluate than their siliciclastic counterparts because many aspects of carbonate rocks make their seismic image signature complex and difficult to interpret. Moreover, the presence of complex overburden such as shallow gas accumulation can exacerbate amplitude and phase fidelity at the reservoir, which introduces an additional imaging challenge. This makes field development of carbonate reservoirs extremely difficult because field development requires detailed delineation of characteristic karst features to avoid drilling hazards and sudden water breakthrough. In this paper, we demonstrate that a tight integration of signal processing, depth model building, and imaging, as well as near-real-time seismic interpretation feedback, is the key to success for imaging complex carbonate reservoirs with overburden challenges. Our findings show that such an integrated approach can result in a substantially better image, reduced depth uncertainty, and better delineation of karst and fractures. It can also aid in well placement and improve reservoir property modeling.

Extended Gassmann equation with dynamic volumetric strain: Modeling wave dispersion and attenuation of heterogeneous porous rocks

Sedimentary rocks are often heterogeneous porous media inherently containing complex distributions of heterogeneities (e.g., fluid patches and cracks). Understanding and modeling their frequency-dependent elastic and adsorption behaviors is of great interest for subsurface rock characterization from multiscale geophysical measurements. The physical parameter of dynamic volumetric strain (DVS) associated with wave-induced fluid flow is proposed to understand the common physics and connections behind known poroelastic models for modeling dispersion behaviors of heterogeneous rocks. We have derived the theoretical formulations of DVS for patchy saturated rock at the mesoscopic scale and cracked porous rock at microscopic grain scales, essentially embodying the wave-induced fluid-pressure relaxation process. By incorporating DVS into the classic Gassmann equation, a simple but practical “dynamic equivalent” modeling approach, the extended Gassmann equation, is developed to characterize the dispersion and attenuation of complex heterogeneous rocks at nonzero frequencies. Using the extended Gassmann equation, the effect of microscopic or mesoscopic heterogeneities with complex distributions on the wave dispersion and attenuation signatures can be captured. Our theoretical framework provides a simple and straightforward analytical methodology to calculate wave dispersion and attenuation in porous rocks with multiple sets of heterogeneities exhibiting complex characteristics. We also demonstrate that, with the appropriate consideration of multiple crack sets and complex fluid patches distribution, the modeling results can better interpret the experimental data sets of dispersion and attenuation for heterogeneous porous rocks.

Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain)

An optimal strategy for building realistic geological models must combine different geophysical techniques, each with its advantages and limitations. However, dealing with multiple geophysical datasets to derive a geological interpretation is not straightforward since geophysical parameters are not always functionally related. In this work, we propose an innovative approach consisting of using machine learning techniques to jointly interpret three geophysical datasets (a pseudo-3D resistivity model, a 3D velocity model, and 4 well-logs). These datasets, among others, were acquired to characterize the suitability of an evaporitic sequence for hosting a temporary storage facility of hazardous radioactive waste, which was planned in Villar de Cañas (Spain). Our strategy consisted of integrating both models in a single 3D bi-parametric grid that nested the velocity and resistivity values in each node. We then used a supervised learning algorithm to lithologically classify each node according to a training set based on the borehole data, which acts as ground truth. The training set is composed of classifiers that lithologically label resistivity-velocity pairs. However, the very shallow nodes lack classifiers due to the poor well-log coverage at the top part of the evaporitic sequence. To fill this gap, we computed an unsupervised cluster analysis that provided new classes to complete the training set. Finally, the supervised classification was applied, providing a new 3D lithology model that is far more consistent with the geology than the models derived from each parameter independently. The 3D model also revealed geological features previously unknown, notably the existence of an inactive fault. The proposed method can be applied to integrate and jointly interpret any kind of multidisciplinary datasets in a wide range of geoscientific problems, including natural resource exploration, geological storage, environmental monitoring, civil engineering practice, and hazard assessment. • The joint interpretation of geophysical data improves subsurface characterization. • Machine learning makes the interpreter's job easier while reducing bias. • Combined supervised and unsupervised learning makes the most of data. • Machine learning to enhance geological interpretation of historic datasets. • Geophysical surveys should be planned anticipating their subsequent integration.

Subsurface Characterization for Secure CO2 Storage in the Pennsylvanian-permian Storage Complex in the Midcontinent Region, U.S.A.

Subsurface characterization of target injection and confining zones is essential for developing a safe and successful Carbon Capture Utilization and Storage (CCUS) project. The Integrated Midcontinent Stacked Carbon Storage Hub (IMSCS-HUB) Project is assessing the feasibility of commercial-scale CCUS implementation in the Midcontinent Region of the United States of America (USA) as part of the U.S. Department of Energy-National Energy Technology Laboratory’s (DOE-NETL) Carbon Storage Assurance Facility Enterprise (CarbonSAFE) initiative. The IMSCS-HUB project area is comprised of a carbon dioxide (CO2) source corridor that runs from eastern Iowa to western Nebraska and a stacked CO2 storage corridor that spans across western Nebraska and Kansas, representing the first large-scale CCUS project for the Midcontinent Region. The deep saline reservoirs and caprocks of the IMSCS-HUB stacked storage corridor subsurface make up a Pennsylvanian-Permian Storage Complex which is characterized by thick stratigraphic successions of marine and non-marine sedimentary rocks, (i.e. cyclothems). These stratigraphic successions provide alternating sequences of deep saline formations, oil-bearing reservoirs, shale, and evaporite units that are conducive to vertically stacked CO2 injection for geologic storage and enhanced oil recovery (EOR). Selected areas within the IMSCS-HUB storage corridor have been identified for detailed CO2 storage site characterization in southwest-central Nebraska (Sleepy Hollow Site), in western Nebraska (Madrid Site), and in western Kansas (Patterson Site). As part of the IMSCS-HUB project, characterization wells were drilled, whole and sidewall core were acquired, advanced geophysical well logs were collected, and well testing was conducted the knowledge gaps at the selected storage sites. Regional and site-specific subsurface analyses of the Pennsylvanian-Permian Storage Complex in the Midcontinent Region are conducted to validate caprock integrity through stacked-injection simulations, and to inform the post-injection monitoring plan. The lateral and vertical extents of the formations of interest are defined through well log analysis and geologic mapping. The stress regime is defined using image log data, mini-fracture tests, and drill stem tests. Petrophysical and geomechanical properties of reservoir and caprock intervals are determined through core laboratory testing, advanced well log analysis (sonic and image logs), and well testing (mini-fracture and drill stem tests). The well testing data is also used to determine the fracture pressure gradient/injection pressure constraints. The core laboratory testing includes routine core analysis (porosity, permeability, net confining stress, and grain density), mercury injection capillary pressure, effective permeability, threshold entry pressure, and triaxial compressive strength. The results of the subsurface characterization are used to construct and populate the static earth model and are key inputs for stacked-injection simulations. Three-dimensional simulations assess the baseline stresses in the subsurface and provide information on the fracture gradient to estimate the maximum permissible injection pressure, simulate the mechanical integrity of the caprocks, and evaluate the risk of induced seismic events using an injection scenario of 50 million metric tons, per the CarbonSAFE objective. The IMSCS-HUB project is supported by the U.S. Department of Energy-National Energy Technology Laboratory Agreement No. DE-FOA-0031623.

Geophysical Investigation Using 3-Dimensional Grid-Formation for Subsurface Lithology Characterization (A Case Study of Ovia North East, Edo State, South South Nigeria)

Geophysical investigations using three-dimensional (3D) grid formation was carried out in Ovia North East Local Government Area of Edo State, Nigeria for subsurface lithology characterisation so as to generate a comprehensive basemap of the study area. Twelve (12) traverses in form of a rectangular grid were occupied for the 2D Electrical Resistivity Imaging (ERI) using the Wenner array. The 2D were all collated to form the 3D grid. The 2D Electrical Resistivity data was processed by the inversion of the 2D apparent resistivity data using the DIPRO software to generate the 2D inverted resistivity section while the 3D inverted resistivity model was done by inverting all the twelve traverses using 3DEarthimager software to model the 3D cube. The results of the 2D ERI revealed three (03) to five (05) resistivity structures across the twelve traverses indicating clay/clayey sand, sand and sandstone on a 200 and 300 m lateral distance and corresponding depth of 39.6 and 57.3 m across each traverses. Resistivity values generally varies from 16.8 – 45302 Ωm across Traverse 1 – 12. The layer horizontal depth slices of the 3D inverted resistivity distribution are in six layers, which are; 0 - 5 m, 5 – 10.8 m, 10.8 – 17.4 m, 17.4 – 25 m, 25 – 33.7 m and 33.7 – 43.8 m. The 3D inverted resistivity model within the study area covered lateral plane (the roll axis), 300 m, in the x plane (the pitch axis), 200 m lateral distance was covered and in the depth plane (the yaw axis), a maximum depth of 66 m is imaged. The inverted 3D Resistivity values generally vary from 189 - 6149 Ωm across the study area. The resistivity structures delineated from the 3D model are clayey sand and sand.

Subsurface Characterization of the Quaternary Active Cheraw Fault in Southeastern Colorado Based on Seismic Imaging

AbstractThe subsurface structure of the late Quaternary active Cheraw fault is relatively unstudied. Vertical displacement estimates of faulted bedrock horizons, characteristics of bedrock structure (e.g., dip), potential association with dissolution of underlying Permian evaporite strata, and whether or not a postulated northeast extension of the topographic scarp is associated with Quaternary faulting have remained open questions. In this study, we assess six 2D seismic reflection profiles that cross the Cheraw fault scarp, demonstrate how Quaternary normal faulting has reactivated pre-existing structures along the northwest flank of the Las Animas arch, and provide new constraints for seismic hazard characterization. We map the fault to depths of at least 1.5–1.8 km into lower Paleozoic strata in which the continuity of the fault through Permian evaporite indicates that dissolution of those stratigraphic intervals has no role in Quaternary surface faulting. Interpretation of the seismic data reveals an ∼75°±5° northwest-dipping fault with ∼24–30 m vertical displacement of upper Cretaceous strata, which coincides with Quaternary scarps at the surface.

Lower Jurassic deformation in the eastern Huincul High, Argentina

Subsurface characterization of the structure and sedimentary sequences in the Agua del Cajón area was performed to understand the Lower Jurassic deformation across the eastern Huincul High. Integration of seismic, well data, and fossil fauna allowed establishing the Late Triassic rifting and Jurassic compressional evolution underwent for this area. Four Late Triassic - Early Jurassic extensional depocenters were recognized, being the most significant the Agua del Cajón and El Salitral depocenters. In general, all of them present an E-W to WNW-ESE orientation and are flanked by NNE to NE-trend transfer zones. The contractional structural style of the Agua del Cajón area consists of south-vergence reverse faults and folds with two primary trends: (i) E-W to WNW-ESE and (ii) NE-SW to NNE-SSW. According to the slip and uplift amount, the first-order structural feature in the study area corresponds to the S-verging, E-W Huincul fault. Geometries and patterns in the seismic reflectors evidence two compressional episodes from late early Toarcian to Callovian time. The first one is associated with the inversion of the previous master normal fault of the El Salitral and Agua del Cajón depocenters during the late early Toarcian. The inversion of the NE-SW synthetic normal fault associated with the El Salitral depocenter also occurred during the first deformation pulses favored by its perpendicular orientation to NW-SE Jurassic σ1. NE-SW fault associated with the Agua del Cajon inversion shows no evidence of inversion-related deformation mechanisms. Instead, the formation of this structure is related to the previous NE-SW transfer zone. After a tectonic quiescence period in late Toarcian – late Aalenian, the compressional activity continued during the late Aalenian and Bajocian evidenced by progressive unconformities into Jurassic sequences. This activity would have continued until Callovian times. These results are in agreement with previously reported evolution in the eastern-central Huincul High region. However, they indicate that the contractional intraplate deformation in the southern Neuquén basin would have begun during the late early Toarcian; this is older than previously proposed.

Robust machine-learning workflow for subsurface geomechanical characterization and comparison against popular empirical correlations

Accurate subsurface geomechanical characterization is critical for fossil and geothermal energy recovery and extraction of earth resources. Compressional and shear travel time logs (DTC and DTS) acquired using sonic logging tools facilitate subsurface geomechanical characterization, such as brittleness, elastic moduli, and rock consolidation. In this study, 8 ‘easy-to-acquire’ conventional well logs were processed using 3 shallow regression-type supervised-learning models, namely ordinary least squares (OLS), multivariate adaptive regression splines (MARS), and artificial neural network (ANN), for depth-wise synthesis of compressional and shear travel times along the length of a well. Among the 6 models, MARS outperforms with R2 of 0.63 and 0.59 when synthesizing the compressional and shear travel times, respectively, in a new, unseen well. ANN models are not as stable as other shallow learning models. The 6 shallow learning models are trained and tested with 8481 data points acquired from a 4240-feet depth interval of a shale reservoir in Well 1, and the trained models are deployed in Well 2 for purposes of blind testing against 2920 data points from 1460-feet depth interval. Apart from the 6 shallow learning models, two widely used empirical models, Han Model (1987) and Castagna model (1985), are implemented and compared with the shallow learning models. Relative error of the MARS model in Well 2 is much smaller than the two empirical models and linear regression model, which indicates that the MARS model performs better than simple statistical and physics-based methods.

Bayesian inversion using nested trans-dimensional Gaussian processes

SUMMARYTo understand earth processes, geoscientists infer subsurface earth properties such as electromagnetic resistivity or seismic velocity from surface observations of electromagnetic or seismic data. These properties are used to populate an earth model vector, and the spatial variation of properties across this vector sheds light on the underlying earth structure or physical phenomenon of interest, from groundwater aquifers to plate tectonics. However, to infer these properties the spatial characteristics of these properties need to be known in advance. Typically, assumptions are made about the length scales of earth properties, which are encoded a priori in a Bayesian probabilistic setting. In an optimization setting, appeals are made to promote model simplicity together with constraints which keep models close to a preferred model. All of these approaches are valid, though they can lead to unintended features in the resulting inferred geophysical models owing to inappropriate prior assumptions, constraints or even the nature of the solution basis functions. In this work it will be shown that in order to make accurate inferences about earth properties, inferences can first be made about the underlying length scales of these properties in a very general solution basis. From a mathematical point of view, these spatial characteristics of earth properties can be conveniently thought of as ‘properties’ of the earth properties. Thus, the same machinery used to infer earth properties can be used to infer their length scales. This can be thought of as an ‘infer to infer’ paradigm analogous to the ‘learning to learn’ paradigm which is now commonplace in the machine learning literature. However, it must be noted that (geophysical) inference is not the same as (machine) learning, though there are many common elements which allow for cross-pollination of useful ideas from one field to the other, as is shown here. A non-stationary trans-dimensional Gaussian Process (TDGP) is used to parametrize earth properties, and a multichannel stationary TDGP is used to parametrize the length scales associated with the earth property in question. Using non-stationary kernels, that is kernels with spatially variable length scales, models with sharp discontinuities can be represented within this framework. As GPs are multidimensional interpolators, the same theory and computer code can be used to solve geophysical problems in 1-D, 2-D and 3-D. This is demonstrated through a combination of 1-D and 2-D non-linear regression examples and a controlled source electromagnetic field example. The key difference between this and previous work using TDGP is generalized nested inference and the marginalization of prior length scales for better posterior subsurface property characterization.

Adapting Petroleum Exploration Tools To Identify and Prioritize Potential CCS Sites, With Application to Texas and Louisiana

Optimization of storage resource is a key parameter in siting and financing storage hubs. We invert petroleum exploration approaches at a geologic play level to assess subsurface risks to long-term storage, as a mechanism to lower cost and increase security. Basin-scale screening, play definition and prospect description are core tasks of petroleum exploration however and there is a well-defined suite of tools to deal with the problem. The work described here adapts those tools to the identification of CCS sites, using the U.S. Gulf of Mexico basin as a timely example. The approach is essentially a three-part one. First, we use regional cross-sections to identify and describe potential storage plays. Second, we use maps of layered surface and subsurface elements to describe the suitability of potential sites, based on the areal quality variation of available reservoirs and seals, the locations of potential traps and CO2 sources, and the distribution of surface constraints such as land usage, available infrastructure and accessibility. Third, we use risk matrices and historical analog data from petroleum exploration to describe the subsurface risks to successful project delivery. With respect to the Gulf of Mexico, CO2 source locations, current land use, site access and the density of existing well penetrations all favor offshore storage. Accordingly, we have focused our efforts on the coastal waters of Texas and Louisiana, constructing a series of exploration-inspired cross-sections and maps that serve both to help adapt the tools of exploration for CCS and to identify and high-grade potential storage sites in coastal waters. The depth window for CO2 injection is defined by subsurface temperature and fluid pressure. Specifically, it lies between the minimum depth for supercritical CO2, which lies at roughly 1km depth and the top of hard over-pressure, which varies between 1.5 and 4km depth, based on extensive drill data. Within this window, stratal ages vary from Early Cretaceous at the northern (onshore) basin edge to Plio-Pleistocene in the south (offshore), reflecting the long-term progradation of depositional systems toward the basin. For each of these stratigraphic intervals, play fairways are defined by a regional seal and its underlying reservoirs. Focussing on the coastal region (i.e. the Oligo-Miocene), we construct a series of maps for each play. The first describes the regional variation in seal capacity (essentially the clay fraction, calibrated by known hydrocarbon columns). The second and third describe reservoir presence and quality, respectively. Historically such maps are based on depositional environment and sediment provenance and they tend to show rather blocky distributions. In an effort to more faithfully represent geologic variability and gradational transitions, we experiment with using modern algorithms and large log databases to create a more variable visualization. Site selection will determine maximum injection rate, ultimate storage capacity and risk of future leakage. In short, regional subsurface characterization is critical to planning investment and may spell the difference between storage project success and failure. Together, the maps developed here describe the regional variation in storage capacity. Combined with maps of surface factors such as accessibility, land ownership, existing land use and CO2 point source locations, they clearly prioritize areas of the basin for investment in infrastructure and development of large-scale storage projects. Adding potential local traps to the map defines a series of concrete leads for storage sites that can be evaluated in more detail as needed for specific projects.

Surface and subsurface characterization of laser-interference structured Ti6Al4V

Ti6Al4V specimens were exposed to high-energy laser pulses of 1.24 J/cm2 fluences per pulse using a two-beam optical configuration to attain laser-interference. In addition to the formation of periodic surface textures by laser-interference, scanning electron microscope (SEM) images demonstrated several microstructural changes on the surface processed. Moreover, the surface and subsurface of Ti6Al4V specimens were characterized for the as-received and laser-interference processed conditions using x-ray photoelectron spectroscopy (XPS) and electron energy dispersive spectroscopy (EDS). The XPS data showed that this laser technique is effective at removal of surface contaminants for Ti6Al4V. The XPS analysis also revealed the formation of an oxy-nitride layer on the laser treated material. The microstructure analysis indicates that grain refinement, dendrite and twin structures were found near the surface due to laser processing.

Source location of volcanic earthquakes and subsurface characterization using fiber-optic cable and distributed acoustic sensing system

We present one of the first studies on source location determination for volcanic earthquakes and characterization of volcanic subsurfaces using data from a distributed acoustic sensing (DAS) system. Using the arrival time difference estimated from well-correlated waveforms and a dense spatial distribution of seismic amplitudes recorded along the fiber-optic cable, we determine the hypocenters of volcanic earthquakes recorded at Azuma volcano, Japan. The sources are located at a shallow depth beneath active volcanic areas with a range of approximately 1 km. Spatial distribution of the site amplification factors determined from coda waves of regional tectonic earthquakes are well correlated with old lava flow distributions and volcano topography. Since DAS observation can be performed remotely and buried fiber-optic cables are not damaged by volcanic ash or bombs during eruptions, this new observation system is suitable for monitoring of volcanoes without risk of system damage and for evaluating volcanic structures.

3D Subsurface Modeling of Multi-Scenario Rock Property and AVO Feasibility Cubes—An Integrated Workflow

A novel inter-disciplinary methodology for the generation of rock property and AVO feasibility maps or cubes to be used in subsurface characterization and prospect de-risking is presented. We demonstrate the workflow for 1D, 2D and 3D cases on data from the North Sea and the Barents Sea, offshore Norway. The methodology enables rapid extrapolation of expected rock physics properties away from well control along selected horizons, constrained by seismic velocity information, geological inputs (basin modeling, seismic stratigraphy and facies maps) and rock physics depth trend analysis. In this way, the expected rock physics properties of a reservoir sandstone (saturated with any pore fluid) can be predicted at any given location between or away from existing wells while honoring rock’s burial and thermal history at this same location. The workflow should allow for more rapid, seamless and geologically consistent subsurface mapping and de-risking of prospects in areas with complex geology and tectonic influence. The AVO feasibility results can furthermore be utilized to generate non-stationary training data for AVO classification.

Petrophysical and mechanical rock property database of the Los Humeros and Acoculco geothermal fields (Mexico)

Abstract. Petrophysical and mechanical rock properties are key parameters for the characterization of the deep subsurface in different disciplines such as geothermal heat extraction, petroleum reservoir engineering or mining. They are commonly used for the interpretation of geophysical data and the parameterization of numerical models and thus are the basis for economic reservoir assessment. However, detailed information regarding petrophysical and mechanical rock properties for each relevant target horizon is often scarce, inconsistent or distributed over multiple publications. Therefore, subsurface models are often populated with generalized or assumed values resulting in high uncertainties. Furthermore, diagenetic, metamorphic and hydrothermal processes significantly affect the physiochemical and mechanical properties often leading to high geological variability. A sound understanding of the controlling factors is needed to identify statistical and causal relationships between the properties as a basis for a profound reservoir assessment and modeling. Within the scope of the GEMex project (EU H2020, grant agreement no. 727550), which aims to develop new transferable exploration and exploitation approaches for enhanced and super-hot unconventional geothermal systems, a new workflow was applied to overcome the gap of knowledge of the reservoir properties. Two caldera complexes located in the northeastern Trans-Mexican Volcanic Belt – the Acoculco and Los Humeros caldera – were selected as demonstration sites. The workflow starts with outcrop analog and reservoir core sample studies in order to define and characterize the properties of all key units from the basement to the cap rock as well as their mineralogy and geochemistry. This allows the identification of geological heterogeneities on different scales (outcrop analysis, representative rock samples, thin sections and chemical analysis) enabling a profound reservoir property prediction. More than 300 rock samples were taken from representative outcrops inside the Los Humeros and Acoculco calderas and the surrounding areas and from exhumed “fossil systems” in Las Minas and Zacatlán. Additionally, 66 core samples from 16 wells of the Los Humeros geothermal field and 8 core samples from well EAC1 of the Acoculco geothermal field were collected. Samples were analyzed for particle and bulk density, porosity, permeability, thermal conductivity, thermal diffusivity, and heat capacity, as well as ultrasonic wave velocities, magnetic susceptibility and electric resistivity. Afterwards, destructive rock mechanical tests (point load tests, uniaxial and triaxial tests) were conducted to determine tensile strength, uniaxial compressive strength, Young's modulus, Poisson's ratio, the bulk modulus, the shear modulus, fracture toughness, cohesion and the friction angle. In addition, X-ray diffraction (XRD) and X-ray fluorescence (XRF) analyses were performed on 137 samples to provide information about the mineral assemblage, bulk geochemistry and the intensity of hydrothermal alteration. An extensive rock property database was created (Weydt et al., 2020; https://doi.org/10.25534/tudatalib-201.10), comprising 34 parameters determined on more than 2160 plugs. More than 31 000 data entries were compiled covering volcanic, sedimentary, metamorphic and igneous rocks from different ages (Jurassic to Holocene), thus facilitating a wide field of applications regarding resource assessment, modeling and statistical analyses.

Linking elastic and electrical properties of rocks using cross-property DEM

SUMMARY Combining electrical and elastic measurements can be instrumental in lowering the uncertainty of subsurface characterization. Many commonly used rock physics relations for joint electrical–elastic properties are at least partly empirical and often rely on the estimation of porosity as an intermediate step. We combine differential effective medium schemes that relate, respectively, elastic and electrical properties to porosity and pore shape. The resulting expressions are independent of porosity, depending only on pore aspect ratio. Analysis of published joint electrical–elastic data shows that a single aspect ratio model performs well for clean sandstones, allowing us to model Vp/Vs ratios as a function of resistivity. Clay-bearing sandstones are more complex, but our modelling can still identify the correct trends. We speculate about the potential to extend our approach to produce additional cross-property relations.

Wavefield reconstruction inversion in the frequency domain of on-ground GPR data

Full waveform inversion (FWI) is one of the most promising techniques for the quantitative characterization of the shallow subsurface of the Earth. However, the conventional FWI with the reduced approach is highly nonlinear, a challenging source of which is the cycle skipping. It would drive FWI to spurious local minima as soon as the initial model parameters are not accurate enough. By introducing a non-negative penalty term and a penalty parameter, wavefield reconstruction inversion (WRI) extends the search space of full waveform inversion, relaxes the requirement to satisfy exactly the wave equation at each iteration, and mitigates some of the problems related to poor start models, which leads to the cycle skipping phenomenon. Moreover, WRI breaks down FWI into two linear sub-problems and reconstruct the wavefield and estimate the model parameter in a loop and there is no need to update and store the wavefield. In this paper, we perform the model reconstruction of on-ground GPR using WRI in the frequency domain by inverting data of a limited number of frequency components, and compare the results of it with that of the conventional FWI. With a limited illumination of the subsurface, WRI with small penalty parameters has a positive performance.