An Integrated Geophysical Approach for Sub Basalt Exploration

The time-lapse joint inversion of geophysical data is required to image the evolution of oil reservoirs during production and enhanced oil recovery, CO2 sequestration, geothermal fields during production, active volcanoes, and the evolution of contaminant plumes. Joint inversion schemes reduce the uncertainty of the model in each monitoring stage, while time lapse inversion algorithms reduce time-related artifacts. There are several approaches that are possible to perform the time-lapse joint inverse problem. In this work, we select the structural joint inversion approach of Meju and Guallardo (the inversion looks for models with strcutural similarities) to perform the joint inversion of DC resistivity and seismic data. Timelapse inversion is performed with an actively timeconstrained (ATC) approach. In this approach, the subsurface is defined as a space-time model. All the snapshots are inverting together assuming a regularization over time of the sequence of snapshots. Still, this approach is flexible enough to allow for abrupt time-related changes in the areas where there are significant indications that these changes are effectively occurring. At the same time, the ATC approach removes inversion artefacts corresponding to spatial artefacts that are randomly distributed over time. We show the advantage of combining strcutural joint inversion and time-lapse inversion using a synthetic case corresponding to cross-hole seismic and DCresistivity data. We show that this approach reduces strongly artefact both with respect to individual inversion of the resistivity and seismic datasets and also with respect to the joint inversion of both data sets at each time step.

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: (1) the reservoir model - principally its permeability magnitude and heterogeneity - and (2) geochemical parameters - primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO{sub 2} storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled ''Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO{sub 2} Injection/Migration in the Weyburn-Midale Project'' (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.

The collection of a range of different seismic data types has greatly improved our understanding of the crustal architecture of Australia's Archaean Yilgarn Craton over the last few years. These seismic data include broadband seismic studies, seismic receiver functions, wide-angle recordings and mine-scale to deep seismic reflection transects. Each data set provides information on the three-dimensional (3D) tectonic model of the Yilgarn Craton from the craton scale through to the mine scale. This paper demonstrates that the integration and rationalisation of these different seismic data sets into a multi-scale 3D geological/seismic model, that can be visualised at once in a single software package, and incorporating all available data sets, significantly enhances this understanding. This enhanced understanding occurred because the integrated 3D model allowed easy and accurate comparison of one result against another, and facilitated the integrated questioning and interrogation across scales and seismic method. As a result, there are feedback questions regarding understanding of the individual seismic data sets themselves, as well as the Yilgarn Craton as a whole. The methodology used, including all the data sets in the model range, had to allow for the wide range of data sets, frequencies and seismic modes. At the craton scale, P-wave, S-wave and surface wave variations constrained the 3D lithospheric velocity model, revealing noticeable large-scale velocity variations within and across the craton. An interesting feature of the data, easily identified in 3D, is the presence of a fast S-wave velocity anomaly (> 4.8 km s − 1 ) within the upper mantle. This velocity anomaly dips east and has a series of step-down offsets that coincide approximately with province and terrane boundaries of the Yilgarn Craton. One-dimensional receiver function profiles show variations in their crustal velocity across the craton. These crustal velocity variations are consistent with the larger-scale geological subdivision of the craton, and provide characteristic profiles for provinces and terranes. The receiver function results and the deep seismic reflection data both agree on the depth to the Moho, and both indicate an increase in Moho depth to the east. The 2D seismic refraction results in the south-west of the craton provide crustal thickness information, an indication of middle and lower crustal compositions, and information regarding the broad-scale architectural framework. At the province- and terrane-scale, the deep seismic reflection data and the mine-scale seismic data provide geometric constraints on crustal architecture, in particular the orientation of the region's fault systems as well as variations in the thickness of the granite–greenstone succession. Integration of the results from wide-angle seismic refraction data coincident with the deep seismic reflection data provided additional constraints on likely upper crustal lithologies. The integrated 3D seismic model implies the dominant geodynamic process involved the development of an orogenic belt that developed with a series of contractional (folding and thrusting) events, separated by equally important extensional events. The seismic reflection data in particular suggests that extensional movement on many shear zones was more common than previously thought. The seismic reflection data suggest that the dominant mineral systems involved deeply sourced fluid flowing up crustal-penetrating shear zones. These deeply sourced fluids were further focussed into sites located above fault-breached domal regions in the upper crust.

Dams are built to contain water or mining tailings and provide several benefits to society. The safety and stability of these structures are essential since eventual accidents can generate catastrophic damage. The main cause of ruptures is associated with abnormal seepage, which can progress to an internal erosion process. To assess the stability and safety of dams, periodic inspections are carried out, which typically include visual inspections and geotechnical instrumentation. Some geophysical methods have the potential to assist conventional geotechnical instrumentation using non-destructive and rapid acquisition investigations. The present study applied a finite element forward modeling to evaluate the percolation conditions and water stability in an earth dam of a decommissioned uranium mine in Brazil. The dam is the final structure of the contention system of mine water treatment. The electrical resistivity data were acquired using electrical resistivity tomography (ERT) and Schlumberger array. The results were presented from the inversion of data in 3D models. The data were processed by open-source software (modeling and inversion of geophysical data). The results indicate the presence of water in the vertical filter located in the crest area. The different levels of investigation indicated water infiltration in the rock mass of the dam foundation. These results are supported by structural surveys carried out in the area that indicate a fracturing pattern in the rock mass of the mine area. The results of the study were satisfactory for the desired purposes and demonstrate that geophysical methods constitute an important complementary tool for the geotechnical monitoring dams.

Resolution of a thin sandwiched layer employing DC resistivity or electromagnetic (EM) sounding methods is a tricky problem and often eludes adequate detectability. The inversion of resistivity data, from various electrode arrays, in general yields the product resistivity x thickness or conductivity x thickness depending on whether the target layer is resistive or conductive. On the other hand EM data, obtained using TE excitation, can resolve a conductive layer to some extent but fails miserably when the target layer is resistive. A joint-inversion of the two data sets (DC resistivity and EM), however, has been found to enhance the resolution of such thin layers to a great extent. In the present study we have tried to assess the performance of resistivity sounding data from various electrode arrays in resolving a thin sandwiched (conductive or resistive) layer, by using joint-inversion with multi-frequency EM data obtained from a large rectangular loop system. The study reveals that a very thin layer or layers with very high contrast and buried at great depths can not be resolved very accurately even by employing the joint-inversion. It is also found that, in general, the joint-inversion of EM data with that from dipole arrays yields better resolution with faster rate of convergence. The study is extended to simulate the field situation by introducing random noise to theoretical response curves. Deterioration of inverted model parameters is observed as the noise level is increased. Dipole arrays are found to provide better estimation of layer parameters in comparison to other arrays.

The use of a fast and integrated model work flow is a part of the IOR strategy, and is expected to lead to reduced uncertainty in reserves predictions and accelerated decision processes. Our basic idea is to have a model chain that is consistent, updateable and repeatable. To provide continuously updated and integrated models with reduced and quantified uncertainty, an integrated approach to reservoir modelling is required. In our integrated model work flow, the following requirements must be fulfilled: •In the model updating, different kinds of data can be incorporated (production data, pressure data and 4D seismic). •The consistency between the geo- and simulation models is maintained. •Model updating and uncertainty modelling are treated as an integrated process.The uncertainty is represented by an ensemble of realizations that are updated in the history matching. •The work flow must allow for fast model updating. The key is “no user interaction” during the updating process. Great care is taken to make each step in the work flow automatic. The fast and integrated model work flow is developed and demonstrated for the Grane field.

One of the major challenges in interpretation of geophysical data remains the ability to jointly invert multiple geophysical datasets for self-consistent 3D earth models of different physical properties. To date, various attempts at 3D joint inversion have been based on either correlations between different physical properties, or by introducing structural similarities. In addition, there could be both physical property and structural correlations between the different earth models, and these complexities cannot be captured by any existing joint inversion techniques. Note that, in practical applications, empirical or statistical correlations between different physical properties may exist, but their specific form may be unknown. In this situation, one can use a method of joint inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. This approach to the joint inversion of multimodal geophysical data uses Gramian spaces of model parameters and Gramian constraints, computed as determinants of the corresponding Gram matrices of the multi-modal model parameters and/or their attributes. This method, recently introduced by Zhdanov et al. (2012), has been shown to be a generalized method of joint inverting any number and combination of geophysical datasets, and includes extant methods based on correlations and/or structural constraints of the multiple physical properties as special case. The method is illustrated by two case studies. We present the results of joint inversion of airborne gravity gradiometer (AGG) and magnetic data collected by Fugro Airborne Surveys in the area of McFaulds Lake located in northwestern Ontario. We also jointly invert airborne magnetic and electromagnetic data from the Lac de Gras region of the Northwest Territories of Canada. These case studies demonstrate how joint inversion using Gramian constraints may enhance subsurface imaging of the mineral targets.

An increase in demand for commodities coupled with a decrease in world-class ore deposit discoveries in the last three decades is the driving force for exploring for a new generation of world-class ore deposits at depth. Exploration through cover is becoming one of the critical challenges for the mineral exploration industry. This paper explores methods of integrating geophysical, geological and landscape data so as to reduce uncertainty in landscape evolution models interpreted from inversion of electromagnetic (EM) data. Inversion of EM data is, in general, non-unique: many different models will be able to fit the EM data equally well, resulting in large uncertainty. However, EM model uncertainty can be significantly constrained when geological and landscape context are taken into account. This study aims to characterise the regolith (weathered and transported cover), and understand how its conductivity varies with the landscape and with the regolith architecture. This is assisted by logging information from 104 boreholes penetrating through the regolith and into the basement rocks. The study area is associated with the DeGrussa Cu-Au deposit located in the Capricorn Orogen of Western Australia, a regolith-dominated terrain where the regolith varies in thickness between <5 and ~150m. We first select EM decay curves, extracted from an airborne EM survey, whose locations are close to those of the boreholes. We then use a layered-earth (1D) forward model, and invert those data for the electrical resistivities of each of the lithologies identified on the geological borehole logs. Layer boundary depths are fixed to the borehole depths. We show how the non-uniqueness associated with EM inversion can be reduced by the inclusion of decay curves from geology with different layer thickness ratios. Lithological models derived from the integration of electromagnetic, geological and landscape data show less uncertainty and are therefore more reliable for mineral exploration targeting.

Three-dimensional numerical models are key tools for reservoir characterization and management. In order to decrease the uncertainty associated with these models, it is essential the integration of all the available data within the geo-modeling workflow. Geostatistical seismic inversion has proven its ability to integrate well-log data and seismic reflection data to infer reliable subsurface elastic models. Recently, the use of electromagnetic data is being used to infer the spatial distribution of the subsurface pore fluid distribution. We present a new iterative geostatistical methodology that is able to jointly invert seismic reflection and electromagnetic data for petro-elastic models, i.e. water saturation, porosity and acoustic impedance. The proposed approach is based on a genetic algorithm that works as a global optimizer to converge the iterative methodology and uses stochastic sequential simulation as the perturbation technique of the model parameter space. A calibrated rock physics model acts as the link between the elastic properties derived from the seismic reflection data and the petrophysical properties derived from the inverted resistivity models. The geostatistical joint inversion of seismic and electromagnetic data was successfully tested and implemented in a synthetic dataset, Stanford VI-E. The inverted petro-elastic models of acoustic impedance, porosity and water saturation agree with the real three-dimensional petro-elastic models in both large- and small- scale details.

Inversion of controlled-source electromagnetic data is dealt with for a geophysical application. The goal is to retrieve a map of conductivity of an unknown body embedded in a layered underground from measurements of the scattered electric field that results from its interaction with a known interrogating wave. This constitutes an inverse scattering problem whose associated forward problem is described by means of electric field domain integral equations. The inverse problem is solved in a Bayesian framework in which prior information is introduced via a Gauss-Markov-Potts model. This model describes the body as being composed of a finite number of different materials distributed into compact homogeneous regions. The posterior distribution of the unknowns is approached by means of the variational Bayesian approximation as a separable distribution that minimizes the Kullback-Leibler divergence with respect to the posterior law. Thus, we get a parametric model for the distributions of the induced currents, the conductivity contrast, and the various parameters of the prior model that are obtained following a semisupervised iterative approach. This method is applied to multifrequency synthetic data corresponding to a 3D crosswell configuration in which the sought body is made of two separated anomalies, a conductive heterogeneity and a resistive one, and its results are compared with that given by the classic contrast source inversion (CSI). The method succeeds in retrieving compact homogeneous regions that correspond to the two anomalies whose shape and conductivities are obtained with a good precision compared with that obtained with CSI.

One of the essential steps in geophysics is the conversion of geophysical data to a map of physical properties something which can only truly be achieved through inversion. While full wavefield inverse methods are widely applied in acoustical imaging, their application to radar has been limited for a number of reasons. These include the complexity of antenna – soil coupling, the fact that antenna radiation patterns are unknown and the underdetermined nature of radar data (in that radar data is in general single rather than multi offset) as well as the large number of parameters we have to invert for. Thus, the majority of current radar inversion efforts are actually kinematic inversions. We investigate the feasibility of a 1D full waveform radar inversion effort. We demonstrate that while we can do 1D radar inversion using a mixed simplex/Powell optimization scheme, the results show a fundamental ambiguity between parameters. From theoretical grounds the extension of the inversion to higher spatial dimensions would seem not to be sufficient to resolve this ambiguity. However, an alternative to 1D inversion is 1D timelapse inversion which is more feasible as it adds a whole new level of information. We implement a first simple form of 1D timelapse inversion on a data set collected in Columbia’s subsurface imaging lab which demonstrates both the success and the potential of this method. Introduction – 4D geophysics As discussed in more detail in [1, 2], time lapse 3D or 4D geophysics is one of the main innovations in the world of geophysics in the last 10 years. Time lapse geophysis has found applications both in global seismic ([3],[4] oil industry ([5-7]) and in near surface geophysics ([8], [9]). The attractiveness of 4D geophysics is that it gives us access to processes – something which for many cases is much more of interest than the pure “static” state of the earth. The principle underlying 4D is easy: we collect multiple identical datasets, remove the complexity of the background (which we can often not invert for anyway) and come up with a time-dependent change in physical properties which can be interpreted as processes. However, a core prerequisite for this is that we can invert the geophysical data to yield physical property maps. Inversion of geophysical data for physical property maps One of the fundamental problems in geophysics is the inversion of geophysical data to find 2D or 3D distributions of physical properties. As is known from extensive work in inverse methods by a range of authors (see e.g the excellent papers in [10]) geophysical inversion is in general a mixed under and overdetermined inverse problem in which we have to apply constraints to arrive at a reasonable solution. These constraints can be minor (e.g. we apply smoothness or minimum norm constraints) or can require us to make a priori statements about either the values or distributions of one or more of the parameters that we seek to estimate. In practice, if the constraints we need to apply to get a good result are

Rational inversion and interpretation of potential field data gives a view of the earth that highlights geological bodies, structures and boundaries. However, data collected in the field contains different kinds of noise that may have a large passive effect on its inversion. In the presented paper, we tried to utilize joint inversion method to reduce influence of noise in the separate inversion of potential field data. We formulate an iterative procedure based on cross gradient function for joint inversion of different geophysical data. Multiple data set give more information about the subsurface and can reduce influence of noise in the inversion of single potential field data set. The method is applied to synthetic data for typical models of gravity and magnetic anomalies. Experimental results show this method is feasible.

Offshore energy-transition activities typically require the construction of new subsurface facilities where overburden heterogeneity and deformation patterns may not be fully understood. Anisotropic marine controlled-source electromagnetic (CSEM) and seismic refraction imaging can be adapted to geohazard assessments of the overburden to mitigate safety and cost risks in such areas. This is investigated using an area in deepwater northwest Borneo as the case study. For this proof-of-concept study, a well-established multiphysics joint inversion code is extended to handle marine seismic refraction data from conventional towed streamers. Realistic field-scale synthetic models with anomalous resistivities and velocities in the shallow section (approximately 400 m below mudline) are built and their CSEM and seismic refraction responses are first inverted separately and then jointly in three dimensions using the cross-gradient method. The joint inversion recovers the anomalous bodies better than the separate inversions. The velocity model from the joint inversion indicates improvement from the separate inversion but requires a good starting model. As a further test of the methodology, first-break traveltimes are picked from legacy 3D seismic data and inverted with the available sparse CSEM data. For model verification, resistivity and velocity profiles from separate and joint inversion models are extracted at a well location in the survey area and compared with actual well logs. The joint inversion models match the well logs better than the separate inversion models. Moreover, known geohazards (complex fault sets and lateral inflows) from independent geomatics study are imaged in the joint inversion models. In addition, the resistivity anisotropy ratio tracks anomalous zones that correspond to areas of recent complex faulting and potential lateral inflows in the overburden mass transport systems. Thus, the joint inversion of CSEM and seismic refraction data has the potential to improve the mapping of overburden hazards offshore, with the associated resistivity anisotropy serving as a proxy for active ground deformation.

In this chapter, a few topics of inversion generally used for interpreting geoelectrical data are discussed briefly. The topics included are: (i) the general introduction on the subject; (ii) Tikhnov’s regularization; (iii) very brief touch on singular value decomposition, least squares inversion, weighted least squares and weighted ridge regression; (iv) Backus–Gilbert inversion; (v) 2D Occam inversion; (vi) REBOCC inversion; (vii) rapid relaxation inversion; (viii) method of steepest descent; (ix) conjugate gradient minimisation; (x) joint inversion; and (xi) appraisal. Topics included in joint inversion are combinations of: (i) magnetotelluric and DC resistivity; (ii) DC resistivity and induced polarization; and (iii) magnetotellurics and seismics. One field example of use of multiple inversion tools for 1D inversion of the same set of data is given.

High Resolution, Simultaneous VSP and Land Seismic Acquisition