摘要 :
Multiphysics inversion exploits different types of geophysical data that often complement
each other and aims to improve overall imaging resolution and reduce uncertainties
in geophysical interpretation. Despite the advantages, ...
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Multiphysics inversion exploits different types of geophysical data that often complement
each other and aims to improve overall imaging resolution and reduce uncertainties
in geophysical interpretation. Despite the advantages, traditional multiphysics inversion
is challenging because it requires a large amount of computational time and intensive
human interactions for preprocessing data and finding trade-off parameters. These
issues make it nearly impossible for traditional multiphysics inversion to be applied as a
real-time monitoring tool for geological carbon storage. In this paper, we present a deep
learning (DL) multiphysics network for imaging CO_2 saturation in real time. The multiphysics
network consists of three encoders for analysing seismic, electromagnetic and
gravity data and shares one decoder for combining imaging capabilities of the different
geophysical data for better predicting CO_2 saturation. The network is trained on pairs
of CO_2 label models and multiphysics data so that it can directly image CO_2 saturation.
We use the bootstrap aggregating method to enhance the imaging accuracy and estimate
uncertainties associated with CO_2 saturation images. Using realistic CO_2 label models
and multiphysics data derived from the Kimberlina CO_2 storage model, we evaluate
the performance of the deep learning multiphysics network and compare its imaging
results to those from the deep learning single-physics networks. Our modelling experiments
show that the deep learning multiphysics network for seismic, electromagnetic,
and gravity data not only improves the imaging accuracy but also reduces uncertainties
associated with CO_2 saturation images. Our results also suggest that the deep learning
multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be
used as an effective low-cost monitoring tool in between regular seismic monitoring.
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摘要 :
Efficient and safe production of hydraulically fractured reservoirs benefits from the prediction of their geometrical attributes. Geophysical methods have the potential to provide data that are sensitive to fracture geometries, al...
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Efficient and safe production of hydraulically fractured reservoirs benefits from the prediction of their geometrical attributes. Geophysical methods have the potential to provide data that are sensitive to fracture geometries, alleviating the typically sparse nature of in situ reservoir observations. Moreover, surface-based methods can be logistically and economically attractive since they avoid operational interference with the injection well infrastructure. This contribution investigates the potential of the surface-based time-domain electromagnetic (EM) method. EM methods can play an important role owing to their sensitivity to injection-induced fluid property changes. Two other advantageous factors are the EM signal-enhancing effect of vertical steel-cased wells and the fact that injected proppants can be enhanced to produce a stronger electrical conductivity contrast with the reservoir's connate fluid. Nevertheless, an optimal fracture characterization will no doubt require the integration of EM and reservoir injection and production data. We hence carry out our investigations within a hydrogeophysical parameter estimation framework where EM data and injection flow rates are combined in a fully coupled way. Given the interdisciplinary nature of coupled hydrogeophysical inverse modeling, we dedicate one section to laying out key aspects in a didactic manner.
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The conductive and capacitive material properties of the subsurface can be quantified through the frequency-dependent complex resistivity. However, the routine three-dimensional (3D) interpretation of voluminous induced polarizati...
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The conductive and capacitive material properties of the subsurface can be quantified through the frequency-dependent complex resistivity. However, the routine three-dimensional (3D) interpretation of voluminous induced polarization (IP) data sets still poses a challenge due to large computational demands and solution nonuniqueness. We have developed a flexible methodology for 3D (spectral) IP data inversion. Our inversion algorithm is adapted from a frequency-domain electromagnetic (EM) inversion method primarily developed for large-scale hydrocarbon and geothermal energy exploration purposes. The method has proven to be efficient by implementing the nonlinear conjugate gradient method with hierarchical parallelism and by using an optimal finite-difference forward modeling mesh design scheme. The method allows for a large range of survey scales, providing a tool for both exploration and environmental applications. We experimented with an image focusing technique to improve the poor depth resolution of surface data sets with small survey spreads. The algorithm's underlying forward modeling operator properly accounts for EM coupling effects; thus, traditionally used EM coupling correction procedures are not needed. The methodology was applied to both synthetic and field data. We tested the benefit of directly inverting EM coupling contaminated data using a synthetic largescale exploration data set. Afterward, we further tested the monitoring capability of our method by inverting timelapse data from an environmental remediation experiment near Rifle, Colorado. Similar trends observed in both our solution and another 2D inversion were in accordance with previous findings about the IP effects due to subsurface microbial activity.
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The fact that the transient electromagnetic (TEM) field is smoothed gradually in space with time allows for a reduced spatial sampling rate of the EM field. On the basis of concepts known from multigrid methods, we have developed ...
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The fact that the transient electromagnetic (TEM) field is smoothed gradually in space with time allows for a reduced spatial sampling rate of the EM field. On the basis of concepts known from multigrid methods, we have developed a restriction operator in order to map the EM field and the material properties from a fine to a coarser finite difference mesh during a forward field simulation with an explicit time-stepping scheme. Two advantages follow. First, the grid size can be reduced. Field restriction involves reducing the number of grid nodes by a factor of 2 for each Cartesian direction. Second, as can be seen from the Courant-Friedrichs-Levy condition, the larger grid spacing allows for proportionally larger time step sizes. After field restriction, a material averaging scheme is employed in order to calculate the underlying effective medium on the coarse simulation grid. Example results show a factor of up to 5 decrease in solution run time, compared to a scheme that uses a constant grid. Key to the accuracy of the approach is knowledge of the proper time range to restrict the fields. An adequate criterion to decide during run time when to restrict involves an error measure for the locations of interest between the fields on the fine mesh and the restricted fields.
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摘要 :
The fact that the transient electromagnetic (TEM) field is smoothed gradually in space with time allows for a reduced spatial sampling rate of the EM field. On the basis of concepts known from multigrid methods, we have developed ...
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The fact that the transient electromagnetic (TEM) field is smoothed gradually in space with time allows for a reduced spatial sampling rate of the EM field. On the basis of concepts known from multigrid methods, we have developed a restriction operator in order to map the EM field and the material properties from a fine to a coarser finite difference mesh during a forward field simulation with an explicit time-stepping scheme. Two advantages follow. First, the grid size can be reduced. Field restriction involves reducing the number of grid nodes by a factor of 2 for each Cartesian direction. Second, as can be seen from the Courant-Friedrichs-Levy condition, the larger grid spacing allows for proportionally larger time step sizes. After field restriction, a material averaging scheme is employed in order to calculate the underlying effective medium on the coarse simulation grid. Example results show a factor of up to 5 decrease in solution run time, compared to a scheme that uses a constant grid. Key to the accuracy of the approach is knowledge of the proper time range to restrict the fields. An adequate criterion to decide during run time when to restrict involves an error measure for the locations of interest between the fields on the fine mesh and the restricted fields.
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In the years 1998, 2000, and 2001, long-offset transient electromagnetic (LOTEM) surveys were carried out at the active volcano Merapi in Central Java. The measurements investigated the conductivity structure of the volcanic edifi...
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In the years 1998, 2000, and 2001, long-offset transient electromagnetic (LOTEM) surveys were carried out at the active volcano Merapi in Central Java. The measurements investigated the conductivity structure of the volcanic edifice. Our area of interest, which is below the summit and the upper flanks, was investigated using horizontal and vertical magnetic field time derivative data from seven transmitter-receiver setups. Because of topography and a three-dimensional (3-D) underground structure, a 3-D interpretation is used. The method optimizes few parameters of a 3-D model by a stable least squares joint inversion of the data, providing sufficient resolution capability. Reasonable data fits are achieved with a nonhorizontally layered model featuring a very conductive basement below depths of 1.5 km. While hydrothermal alteration is also considered, we tentatively explain the high conductivities by aqueous solutions with relatively high salt contents. A large magma body or a small superficial reservoir below Merapi's central volcanic complex, as discussed by other authors, cannot be resolved by the LOTEM data.
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Rectangular grid cells are commonly used for the geophysical modeling of gravity anomalies, owing to their flexibility in constructing complex models. The straightforward handling of cubic cells in gravity inversion algorithms all...
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Rectangular grid cells are commonly used for the geophysical modeling of gravity anomalies, owing to their flexibility in constructing complex models. The straightforward handling of cubic cells in gravity inversion algorithms allows for a flexible imposition of model regularization constraints, which are generally essential in the inversion of static potential field data. The first part of this paper provides a review of commonly used expressions for calculating the gravity of a right polygonal prism, both for gravity and gradiometry, where the formulas of Plouff and Forsberg are adapted. The formulas can be cast into general forms practical for implementation. In the second part, a weighting scheme for resolution enhancement at depth is presented. Modelling the earth using highly digitized meshes, depth weighting schemes are typically applied to the model objective functional, subject to minimizing the data misfit. The scheme proposed here involves a non-linear conjugate gradient inversion scheme with a weighting function applied to the non-linear conjugate gradient scheme's gradient vector of the objective functional. The low depth resolution due to the quick decay of the gravity kernel functions is counteracted by suppressing the search directions in the parameter space that would lead to near-surface concentrations of gravity anomalies. Further, a density parameter transformation function enabling the imposition of lower and upper bounding constraints is employed. Using synthetic data from models of varying complexity and a field data set, it is demonstrated that, given an adequate depth weighting function, the gravity inversion in the transform space can recover geologically meaningful models requiring a minimum of prior information and user interaction.
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Offshore seismic and electromagnetic (EM) imaging for hydrocarbons can require up to tens of millions of parameters to describe the 3D distribution of complex seabed geology and relevant geophysical attributes. The imaging and dat...
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Offshore seismic and electromagnetic (EM) imaging for hydrocarbons can require up to tens of millions of parameters to describe the 3D distribution of complex seabed geology and relevant geophysical attributes. The imaging and data volumes for such problems are enormous. Descent-based methods are the only viable imaging approach, where it is often challenging to manage the convergence of stand-alone seismic and EM inversion experiments. When a joint seismic- EM inversion is implemented, convergence problems with descent-based methods are further aggravated. Moreover, resolution mismatches between seismic and EM pose another challenge for joint inversion. To overcome these problems, we evaluated a coupled seismic-EM inversion workflow and applied it to a set of full-wave-seismic, magnetotelluric (MT) and controlled-source electromagnetic (CSEM) data for subsalt imaging. In our workflow, we address disparate resolution properties between seismic and EM data by implementing the seismic inversion in the Laplace domain, where the wave equation is transformed into a diffusion equation. The resolution of seismic data thus becomes comparable to that of EM data. To mitigate the convergence problems, the full joint seismic-EMinverse problem is split into manageable components: separate seismic and EM inversions and an intermediate step that enforces structural coupling through a cross-gradient-only inversion and resistivity-velocity crossplots. In this workflow, stand-alone seismic and MT inversion are performed first. The cross-gradient-only inversion and the crossplots are used to precondition the resistivity and velocity models for subsequent stand-alone inversions. By repeating the sequence of the stand-alone seismic, MT, and cross-gradient- only inversions along with the crossplots, we introduce the seismic structural information into the resistivity model, and vice versa, significantly improving the salt geometry in both resistivity and velocity images. We conclude that the improved salt geometry can then be used to precondition a starting model for CSEM inversions, yielding significant improvement in the resistivity images of hydrocarbon reservoirs adjacent to the salt.
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Formation anisotropy should be incorporated into the analysis of controlled-source electromagnetic CSEM data because failure to do so can produce serious artifacts in the resulting resistivity images for certain data configuration...
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Formation anisotropy should be incorporated into the analysis of controlled-source electromagnetic CSEM data because failure to do so can produce serious artifacts in the resulting resistivity images for certain data configurations of interest. This finding is demonstrated in model and case studies. Sensitivity to horizontal resistivity will be strongest in the broadside electric field data where detectors are offset from the tow line. Sensitivity to vertical resistivity is strongest for overflight data where the transmitting antenna passes directly over the detecting antenna. Consequently, consistent treatment of overflight and broadside electric field measurements requires an anisotropic modeling assumption. To produce a consistent resistivity model for such data, we develop and use a 3D CSEM imaging algorithm that treats transverse anisotropy. The algorithm is based on nonlinear conjugate gradients and full wave-equation modeling. It exploits parallel computing systems to effectively treat 3D imaging problems and CSEM data volumes of industrial size.We use it to demonstrate the anisotropic imaging process on model and field data sets from the North Sea and offshore Brazil. We also verify that isotropic imaging of overflight data alone produces an image generally consistent with vertical resistivity. However, superior data fits are obtained when the same overflight data are analyzed assuming an anisotropic resistivity model.
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Large-scale controlled-source electromagnetic (CSEM) three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical-conductivity mapping of potential offshore oil and gas reservoirs. To cope with...
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Large-scale controlled-source electromagnetic (CSEM) three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical-conductivity mapping of potential offshore oil and gas reservoirs. To cope with the typically large computational requirements of the 3D CSEM imaging problem, our strategies exploit computational parallelism and optimized finite-difference meshing. We report on an imaging experiment utilizing 32,768 tasks (and processors) on the IBM Blue Gene/L (BG/L) supercomputer at the IBM T. J. Watson Research Centre.
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