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Gravimetry is a routine part of the geophysicists toolset, historically used in geophysics following the geodetic definitions of gravity anomalies and their related “reductions”. Several authors have shown that the geodetic concept of a gravity anomaly does not align with goals of gravimetry in geophysics (the investigation of anomalous density distributions). Much of this confusion likely stems from the lack of widely available tools for performing the corrections needed to arrive at a geophysically meaningful gravity disturbance. For example, free-air corrections are completely unnecessary since analytical expressions for theoretical gravity at any point have existed for over a decade. Since this is not easily done in a spreadsheet or short script, modern tools for processing and modelling gravity data for geophysics are needed. These tools must be trustworthy (i.e., extensively tested) and designed with software development and geophysical best practices in mind.We present the Python libraries Harmonica and Boule, which are part of the Fatiando a Terra project (https://www.fatiando.org). Both tools are open-source under the permissive BSD license and are developed in the open by a community of geoscientists and programmers.Harmonica provides tools for processing, forward modelling, and inversion of gravity and magnetic data. The first release of Harmonica was focused on implementing methods for processing and interpolation with the equivalent source technique, as well as forward modelling with right-rectangular prisms, point sources, and tesseroids. Current work is directed towards implementing a processing pipeline for gravity data, including topographic corrections in Cartesian and spherical coordinates, atmospheric corrections, and more. The software is still in early stages of development and design and would benefit greatly from community involvement and feedback.Boule implements reference ellipsoids (including oblate ellipsoids, spheres, and soon triaxial ellipsoids), conversions between ellipsoidal and geocentric spherical coordinates, and normal gravity calculations using analytical solutions for gravity fields at any point outside of the ellipsoid. It includes ellipsoids for the Earth as well as other planetary bodies in the solar system, like Mars, the Moon, Venus, and Mercury. This enables the calculation of gravity disturbances for Earth and planetary data without the need for free-air corrections. Boule was created out of the shared needs of Harmonica, SHTools (https://github.com/SHTOOLS), and pygeoid (https://github.com/ioshchepkov/pygeoid) and is developed with input from developers of these projects.We welcome participation from the wider geophysical community, irrespective of programming skill level and experience, and are actively searching for interested developers and users to get involved in shaping the future of these projects.

Machine learning is becoming an appealing tool in various fields of earth sciences, especially in resources estimation. Six machine learning algorithms have been used to predict the presence of gold mineralization in drill core from geophysical logs acquired at the Lalor deposit, Manitoba, Canada. Results show that the integration of a set of rock physical properties — measured at closely spaced intervals along the drill core — with ensemble machine learning algorithms allows the detection of gold-bearing intervals with an adequate rate of success. Since the resulting prediction is continuous along the drill core, the use of this type of tool in the future will help geologists in selecting sound intervals for assay sampling and in modeling more continuous ore bodies during the entire life of a mine.

Deployment of carbon capture and storage on a large scale poses many challenges. One is the availability of reservoirs with suitable characteristics (high porosity and permeability, matched large emitters, etc.). Such a challenge might lead to the selection of reservoirs with relatively low porosity. In those environments, the rock matrix is often stiff and questions arise regarding the capabilities of seismic methods to monitor math formula injection. An illustrative modeling of seismic reflectivities within a hypothetical geological formation with physical properties equal to that of the low-porosity reservoir in the St. Lawrence Lowlands sedimentary basin, suggests that a math formula liquid-water contact is a potentially good seismic reflector despite a priori unfavorable conditions.

Deployment of carbon capture and storage on a large scale poses many challenges. One is the availability of reservoirs with suitable characteristics (high porosity and permeability, matched large emitters, etc.). Such a challenge might lead to the selection of reservoirs with relatively low porosity. In those environments, the rock matrix is often stiff and questions arise regarding the capabilities of seismic methods to monitor CO 2 injection. In this paper, a series of ultrasonic measurements were performed under various pressure and temperature conditions to test the seismic response of two low-porosity reservoir samples of the St Lawrence Lowlands sedimentary basin to different CO 2 phases. Results show that the P-wave velocity and amplitude can be used to detect the phase transition. Illustrative modeling of seismic reflectivities within a hypothetical geological formation with physical properties equal to that of the St Lawrence Lowlands sedimentary basin, suggests that a CO 2 liquid-water contact is a potentially good seismic reflector. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd.

We present a two-step stochastic inversion approach for monitoring the distribution of CO2 injected into deep saline aquifers for the typical scenario of one single injection well and a database comprising a common suite of well logs as well as time-lapse vertical seismic profiling (VSP) data. In the first step, we compute several sets of stochastic models of the elastic properties using conventional sequential Gaussian co-simulations (SGCS) representing the considered reservoir before CO2 injection. All realizations within a set of models are then iteratively combined using a modified gradual deformation algorithm aiming at reducing the mismatch between the observed and simulated VSP data. In the second step, these optimal static models then serve as input for a history matching approach using the same modified gradual deformation algorithm for minimizing the mismatch between the observed and simulated VSP data following the injection of CO2. At each gradual deformation step, the injection and migration of CO2 is simulated and the corresponding seismic traces are computed and compared with the observed ones. The proposed stochastic inversion approach has been tested for a realistic, and arguably particularly challenging, synthetic case study mimicking the geological environment of a potential CO2 injection site in the Cambrian-Ordivician sedimentary sequence of the St. Lawrence platform in Southern Quebec. The results demonstrate that the proposed two-step reservoir characterization approach is capable of adequately resolving and monitoring the distribution of the injected CO2. This finds its expression in optimized models of P- and S-wave velocities, density, and porosity, which, compared to conventional stochastic reservoir models, exhibit a significantly improved structural similarity with regard to the corresponding reference models. The proposed approach is therefore expected to allow for an optimal injection forecast by using a quantitative assimilation of all available data from the appraisal stage of a CO2 injection site.

Machine learning is a popular topic in geosciences at the moment. It allows the management and interpretation of data in quantities and varieties (number of variables) that a human being would not be able to achieve. Rock physical properties acquired along drillholes can be used to generate predictions about the nature and characteristics of the rock when wireline logging is taking place. In this paper, we investigate the accuracy of facies prediction using machine learning algorithms by automatically interpreting geological rock types along drillholes from rock physical properties. A data-processing workflow is proposed to enhance the prediction power of the geophysical measurements, a model calibration approach is outlined and predictions on test data are presented. Results show more than 80% of correspondence between the automated prediction and the geologist interpretation.

Modeling of grades is a key step and the major source of error in appraisal stage of mining projects. We used a geostatistical approach to explicitly integrate seismic travel time data, as well as acoustic and core logging data into the estimation of nickel grades in the Voisey's Bay deposit. Firstly, the crosshole seismic travel times are inverted using a stochastic tomographic algorithm. This algorithm allows for the inclusion of acoustic log data and seismic covariance into the inverse problem, leading to high-resolution velocity tomographic images of the orebody. Secondly, grade realizations are generated using a Bayesian sequential Gaussian simulation algorithm, which integrates the ore grades measured on the core logs and the previously inverted tomographic data. The application of the presented method to the Voisey's Bay deposit yields an improved knowledge of the geology setting and generates grade models with realistic spatial variability compared to conventional methods.

We have used surface-based electrical resistivity tomography to detect and characterize preferential hydraulic pathways in the immediate downstream area of an abandoned, hazardous landfill. The landfill occupies the void left by a former gravel pit and its base is close to the groundwater table and lacking an engineered barrier. As such, this site is remarkably typical of many small- to medium-sized waste deposits throughout the densely populated and heavily industrialized foreland on both sides of the Alpine arc. Outflows of pollutants lastingly contaminated local drinking water supplies and necessitated a partial remediation in the form of a synthetic cover barrier, which is meant to prevent meteoric water from percolating through the waste before reaching the groundwater table. Any future additional isolation of the landfill in the form of lateral barriers thus requires adequate knowledge of potential preferential hydraulic pathways for outflowing contaminants. Our results, inferred from a suite of tomographically inverted surfaced-based electrical resistivity profiles oriented roughly perpendicular to the local hydraulic gradient, indicate that potential contaminant outflows would predominantly occur along an unexploited lateral extension of the original gravel deposit. This finds its expression as a distinct and laterally continuous high-resistivity anomaly in the resistivity tomograms. This interpretation is ground-truthed through a litholog from a nearby well. Since the probed glaciofluvial deposits are largely devoid of mineralogical clay, the geometry of hydraulic and electrical pathways across the pore space of a given lithological unit can be assumed to be identical, which allows for an order-of-magnitude estimation of the overall permeability structure. These estimates indicate that the permeability of the imaged extension of the gravel body is at least two to three orders-of-magnitude higher than that of its finer-grained embedding matrix. This corroborates the preeminent role of the high-resistivity anomaly as a potential preferential flow path.

The success of the geological storage of CO2 depends on the capability to monitor movements of the injected fluid into the subsurface. For understanding the effects of CO2 as a pore fluid on the overall rock seismic response, a series of ultrasonic measurements on two samples of saline aquifer sandstones of the Potsdam Group have been made showing clear P-wave variations with CO2 varying phase state. Significant amplitude variation was only observed in the Covey Hill sample. The laboratory measurements and well log data were used to calibrate a numerical model that was used to perform poro-viscoelastic forward modeling of time-lapse walkaway VSP surveys. The results show that supercritical CO2 injected in the reservoir, results in a clear seismic signature. Finally, the modeling results also indicate that the possibility to rely on AVO analysis to monitor the CO2 plume is compromised by a refracted wave that appear early in the wave field.