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Keywords: marine csem data

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Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018

Paper Number: SEG-2018-2997574

... finite-element method (EB-FEM), and adopt conjugate gradient method to solve the Gauss-Newton inversion equation. The final model update is searched by a linear algorithm. We test our inversion code on synthetic

**marine****CSEM****data**generated from a reservoir model with complex seabed topography...
Abstract

ABSTRACT In this paper, we propose a 3D regularized inversion method for frequency-domain marine controlled-source electromagnetic (CSEM) data. In this method, the model is discretized by unstructured tetrahedral grids, which is suitable for simulating complex topography. We calculate the response and sensitivity of marine CSEM using edge-based finite-element method (EB-FEM), and adopt conjugate gradient method to solve the Gauss-Newton inversion equation. The final model update is searched by a linear algorithm. We test our inversion code on synthetic marine CSEM data generated from a reservoir model with complex seabed topography. The inversion results show that the inversion method proposed in this paper can accurately recover the position and resistivity distribution of the resistive reservoir at high efficiency. Presentation Date: Tuesday, October 16, 2018 Start Time: 9:20:00 AM Location: Poster Station 15 Presentation Type: Poster

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2016 SEG International Exposition and Annual Meeting, October 16–21, 2016

Paper Number: SEG-2016-13858558

... International , 185 , 622 – 636 , 10.1111/j.1365-246X.2011.04974.x . Lu , X. , and C. Xia , 2007 , Understanding anisotropy in

**marine****CSEM****data**: 77th Annual International Meeting, SEG, Expanded Abstracts , 633 – 637 , 10.1190/1.2792498 . Maaø , F. A. , 2007...
Abstract

ABSTRACT We present a 3D inversion algorithm for marine CSEM data which takes the electrical anisotropy of steeply-dipping sedimentary rock formations into account. This anisotropy can have a significant impact on CSEM data, and we show that failure to take it into account may lead to imaging artifacts, which complicates the interpretation of the resistivity image. We apply our algorithm to both synthetic data and a field data set acquired over the Perdido fold belt in the Gulf of Mexico, and compare the results from VTI and TTI inversions. Presentation Date: Thursday, October 20, 2016 Start Time: 11:25:00 AM Location: 141 Presentation Type: ORAL

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2013 SEG Annual Meeting, September 22–27, 2013

Paper Number: SEG-2013-1439

... reservoir simulation Artificial Intelligence Upstream Oil & Gas Reservoir Characterization history matching Bayesian Inference Scarborough gas field rj-mcmc inversion noise correlated error

**marine****csem****data**hierarchical bayesian inversion horizontal line probability correlation seg...
Abstract

SUMMARY Uncertainty in the transmitter position, theory error and insufficient model parameterization amongst various other factors can lead to significant correlated error in observed controlled source electromagnetic data. These errors come to light by an examination of the residuals after performing inversion. Since correlated error violates the assumption of independent data noise it can manifest in spurious structure in inverted models. We demonstrate this using both synthetic data and real data from Scarborough gas field, North West Australia. In this work we propose a method which uses a hierarchical Bayesian framework and reversible jump Markov chain Monte Carlo to account for correlated error. We find that this removes suspect structure from the inverted models and within reasonable prior bounds, provides information on the resolution of resistivity at depth.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2013 SEG Annual Meeting, September 22–27, 2013

Paper Number: SEG-2013-0083

... resistivity of the background media, but not to resolving thin horizontal resistors. As a result, the resolution of

**marine****CSEM****data**to reservoirs is lower in anisotropic media than for the isotropic case, unless some possible relation between the two resistivities is accounted for in anisotropic inversion...
Abstract

Summary This paper presents an anisotropic 3D inversion algorithm for marine controlled-source electromagnetic (CSEM) data with the assumption that electrical anisotropy is represented by the vertical and horizontal resistivities. For such anisotropic media, the horizontal and vertical electric fields are related to the horizontal and vertical resistivities, respectively. In addition, the horizontal electric field is insensitive to thin, horizontal resistors associated with hydrocarbon reservoirs. Thus, the horizontal electric field contributes mainly to defining the horizontal resistivity of the background media, but not to resolving thin horizontal resistors. As a result, the resolution of marine CSEM data to reservoirs is lower in anisotropic media than for the isotropic case, unless some possible relation between the two resistivities is accounted for in anisotropic inversion. Synthetic examples show that imposing the equality of the two resistivities is important in stabilizing the inversion process and improving the resolution of the reservoir. It is also shown that the reservoir at a depth of 3 km below the seafloor can be well delineated from three-frequencies (0.1, 0.3, and 0.5 Hz) data in deep water (1 km deep), while additional lower frequency (0.05 Hz) data are required to detect the same reservoir in shallow water (100 m deep).

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2012 SEG Annual Meeting, November 4–9, 2012

Paper Number: SEG-2012-0141

... shallow-water

**marine****csem****data**vertical resistivity inline inversion**marine****csem****data**reservoir characterization thin resistor csem data resolution resistivity frequency single frequency central line horizontal resistivity geophysics depth resolution deep thin resistor broadside data...
Abstract

Summary With increasing applications of the marine controlled-source electromagnetic (CSEM) surveying to offshore hydrocarbon explorations in more complex geologic and shallow-water settings, 3D inversion is expected to play a more important role in detecting and delineating hydrocarbon reservoirs. However, there seems to be no consensus about the capability of inversion to resolve thin resistors representative of hydrocarbon reservoirs, because very few synthetic data studies have been presented for deep targets in shallow-water environments. This study examines how Gauss-Newton-based 3D inversion can resolve deep thin resistors (2000 m below the seafloor) in a shallow-water setting (100 m deep) for different frequencies and source-receiver configurations and how the resolution differs for isotropic and anisotropic cases. It is shown that inversion of multiple frequencies recovers the reservoir much better than inversion of single frequencies, and that jointly inverting inline and broadside data is essential for improving the depth resolution. A comparison of isotropic and anisotropic inversion results reveals that for anisotropic media, the indication of the reservoir is as significant in the vertical resistivity as that for isotropic media, but that the depth resolution to the target is much lower. This is because the horizontal field contributes mainly to defining the horizontal resistivity, but not to resolving thin resistors in the vertical resistivity.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2012 SEG Annual Meeting, November 4–9, 2012

Paper Number: SEG-2012-1347

... oil & gas synthetic data resistivity anomaly interpretation resistivity receiver

**marine****csem****data**anomaly lucy macgregor frequency normalized anomalous electric field ackermann reservoir characterization inversion geophysics rock solid image 2D Inversions of 3D**Marine****CSEM****Data**...
Abstract

Summary A combination of 3D forward simulations and 2D and 3D inversions have been used to demonstrate the appropriateness of applying 2D interpretation on data due to a 3D resistivity anomaly for a marine CSEM survey. This study is done in the first instance on a simple benchmark model. The results of synthetic modeling and inversion tests quantitatively show that the most important factors to properly use 2D inversions to characterize a 3D anomaly are: magnitude of the target’s CSEM response, the geometry, and the distance between its edge and the survey line.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2009 SEG Annual Meeting, October 25–30, 2009

Paper Number: SEG-2009-0684

...., the bathymetry effect) and keep it unchanged during the entire modeling and migration process. The method is illustrated by numerical examples of modeling and inversion of

**marine****CSEM****data**in areas with rough bathymetry. Introduction During recent years, the marine controlled-source electromagnetic (MCSEM...
Abstract

Summary In this paper we present a new approach to the interpretation of the marine controlled-source electromagnetic (MCSEM) data in areas with rough bathymetry. This approach is based on a new formulation of the integral equation EM modeling method in models with inhomogeneous background conductivity. The developed technique allows us to incorporate known geological structures and bathymetry effects in the method of iterative EM migration/holographic imaging and inversion. This approach provides us with the ability to precompute only once the effect of a known geoelectrical structure (e.g., the bathymetry effect) and keep it unchanged during the entire modeling and migration process. The method is illustrated by numerical examples of modeling and inversion of marine CSEM data in areas with rough bathymetry. Introduction During recent years, the marine controlled-source electromagnetic (MCSEM) method has become widely used for active geophysical surveying of sea-bottom geological structures in hydrocarbon exploration. The interpretation of MCSEM data over complex 3D geoelectrical structures is a very challenging problem. This problem becomes even more complicated in areas with rough sea-bottom bathymetry, because the relief of a sea bottom makes a profound effect on the EM data observed by the receivers located in close proximity to the bottom. In this paper we introduce a new approach to interpretation of MCSEM data in areas with rough bathymetry. This approach is based on a new formulation of the integral equation (IE) EM modeling method in models with inhomogeneous background conductivity (Zhdanov et al., 2006). The developed technique allows us to incorporate known geological structures and bathymetry effects in the method of iterative EM migration/holographic imaging and inversion. This approach provides us with the ability to precompute only once the effect of the known geoelectrical structure (e.g., the bathymetry effect) and keep it unchanged during the entire modeling and migration process. Taking into account that precomputing the bathymetry effect constitutes the most time-consuming part of the EM modeling, this approach allows us to increase the effectiveness of the interpretation of the MCSEM data significantly Accounting for bathymetry using EM migration with the inhomogeneous background conductivity (IBC) method A marine CSEM survey typically consists of an array of receivers, which record the response of the earth to EM signals transmitted by single or multiple transmitters. Figure 1 illustrates the principles of EM migration in a model with inhomogeneous background conductivity (IBC). We consider a 3D geoelectrical model with horizontally layered (normal) conductivity s n , inhomogeneous background conductivity s b = s n + ?s b within a domain D b , and anomalous conductivity ?s a within a domain D a (Figure 1). The model is excited by an EM field generated by an arbitrary transmitter which is time-harmonic as e -i ? t . The EM field is measured by a set of electric and/or magnetic field receivers, as shown in Figure 1. The goal is to develop a method of migration of the EM field recorded by the receivers in order to generate an image of the anomalous conductivity distribution. According to the basic principles of the integral equation method with inhomogeneous background conductivity (IE IBC (Zhdanov et al., 2006)

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2009 SEG Annual Meeting, October 25–30, 2009

Paper Number: SEG-2009-0825

...) for joint interpretation with seismic data (see also additional references in Zach et al. (2008a)). annual international meeting international exposition resistivity seg houston 2009 marine csem inversion

**marine****csem****data**prospect exploration case study receiver csem gulf mexico basin...
Abstract

Introduction Summary High salt concentrations and small targets in complicated geology often found in the Gulf of Mexico have been an impediment to the application of marine CSEM techniques, until recent advances in operations, acquisition hardware and advanced processing techniques permit 3D-mapping of complex resistivity distributions. Using two examples from a recent prelease sale campaign, we demonstrate the successful application of the entire value chain from customized acquisition grids, multi-frequency acquisition to processing of data including wide-azimuth lines with reliable phase and amplitude, and subsequent inversion-based 3D interpretation. Advanced interpretation is based on an iterative Hessian-based inversion with quasi-Newton update and fast finite-difference time-domain modeling. Inversion results are used to construct resistivity distribution consistent with 3D-seismic images. During the campaign, we successfully addressed the client’s need to resolve small targets (2 km x 2 km) with low-resistivity pay (??<5 Om), many found in the vicinity (<1 km) of large salt bodies. Marine controlled-source electromagnetic (CSEM) methods for hydrocarbon detection relying on a horizontal electric bipole (HED) emitting a predefined low frequency spectrum (0.05-10 Hz), and the recorded electromagnetic fields by ocean bottom receivers, have been used in hydrocarbon exploration on a commercial scale since 2002 (Eidesmo et al., 2002). The sensitivity to hydrocarbons is due to the relative enhancement of the transverse magnetic component of the received electromagnetic signal through a partial waveguide effect by buried resistors, which can be either hydrocarbon deposits or other resistive bodies. Marine CSEM has become a method for 3D imaging of areas with complex geologies, which is applied by many major oil companies, either as a stand-alone frontier exploration tool (Monk et al., 2008; Suffert et al., 2008) or in conjunction with, or addition to other geophysical probes. Recent published case studies for the latter include Carrazone et al. (2008), Price et al. (2008) Plessix and van der Sman (2008), Zach and Frenkel (2009), and Zach et al.(2009). Advances in hardware and operations have resulted in a vast improvement in data quality, permitting the acquisition of well-defined and repeatable grids of seabed receivers with complex towing patterns including the acquisition of wide-azimuth data with consistent magnitude and phase (Zach et al., 2008b). Such high-fidelity physical measurements enable the effective use of 3D inversion techniques. They allow for imaging of multiple resistive bodies in the subsurface. The majority of 3D CSEM inversion techniques rely on iterative optimization where the gradient of a misfit functional with respect to a discrete conductivity grid is computed during each iteration. The present approach employs the quasi-Newton method described in Zach et al. (2008a). It is based on the gradient calculation developed by Støren et al. (2008) and the fast finite-difference time-domain modeling code by Maaø (2007). Other notable recent contributions to CSEM inversion methodology include Commer and Newman (2008) on joint CSEM and MT inversion, Jing et al. (2008), which shows the importance of anisotropy in many surveys, as well as Norman et al. (2008) for joint interpretation with seismic data (see also additional references in Zach et al. (2008a)).

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2008 SEG Annual Meeting, November 9–14, 2008

Paper Number: SEG-2008-0687

..., and the methodology is tested on a synthetic data and a dataset of a MCSEM survey conducted in the Troll West Gas Province(TWGP). Inversion of the Troll field

**Marine****CSEM****data**The data set from Troll gas field is provided by EMGS. It is the largest gas field in the Norway Continental Plate. The survey...
Abstract

Summary Marine controlled-source electromagnetic (MCSEM) is a recently developed method for the offshore hydrocarbon detection. In this paper, we introduce the study of the 3D inversion for MCSEM data with the Born approximation method. The inversion speed is fairly improved by using reciprocal theory in the forward processing. The electrical character and geometric size of synthetic EM data inversion results show good agreement with the model. The methodology is also tested on the dataset of an MCSEM survey conducted in the Troll West Gas Province (TWGP), and the interpretation is satisfactory. Therefore, the Born approximation is an effective approach to improve the interpretation precision for the MCSEM data set. Introduction Statoil carried out the trial survey (Ellingsrud et al, 2002) of the marine controlled-source electromagnetic method with the purpose of directly detecting hydrocarbon in the deep sea. The released cases show that MCSEM is an effective approach to the detection of marine reservoirs with high saturation up to 60% to 70%. The successful cases drew much attention from the worldwide oil and gas companies. Since then, the marine electromagnetic method went into a rapidly developing stage. This method reduces both the risk and the cost of marine hydrocarbon exploration significantly. Both modeling and inversion are the two keys to the final interpretation which may directly influence the survey effect. 1D and 2D were used in the past, while 3D has been rapidly developed in recent years with development in computer and theories. Commer and Newman(2007) developed the three-dimensional finite-difference inversion algorithm which takes the bathymetry into consideration and Zhdanov et al. (2007) adopted a three-dimensional regularized focusing algorithm with the minimum vertical support stabilizer for the 3D inversion of MCSEM dataset. The Born approximation can be adopted to avoid solving the super-large system of linear equations for the full integration equation algorithm. The algorithm substitutes the background field, where the target heterogeneity stays as the total field, at the heterogeneity - thus, solving a system of linear equations can be avoided. In addition, the speed of modeling can be significantly increased. By using the principle of reciprocity, we can decrease the time consumption of simulating multi-transmitters with the IE method, thereby speeding the 3D inversion. The paper presents the study about the 3D MCSEM inversion with the Born approximation and the re-weighed regularized conjugate gradient (RRCG) method (Zhdanov, 2002). The inversion speed is fairly improved by using reciprocal theory in the forward processing, and the methodology is tested on a synthetic data and a dataset of a MCSEM survey conducted in the Troll West Gas Province(TWGP). Inversion of the Troll field Marine CSEM data The data set from Troll gas field is provided by EMGS. It is the largest gas field in the Norway Continental Plate. The survey is deployed at the shallow water of the northeastern Offshore North Sea, where the water is about 300 to 360 m deep. The data acquisition was accomplished jointly by EMGS and Statoil in 2003. The survey area covers the entire Troll West Gas Province (TWGP) (Johansen et al, 2005).

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2008 SEG Annual Meeting, November 9–14, 2008

Paper Number: SEG-2008-0614

... for the optimization, where the Hessian matrix is approximated with BFGS matrices using the gradient projection method (Zhu et al., 1997, Byrd et al., 1995). shallow subsurface subsurface geophysics optimization

**marine****csem****data**inversion synthetic data fast finite-difference time-domain forward code...
Abstract

Summary An inversion methodology for marine controlled-source electromagnetic (MCSEM) data with approximate Hessianbased optimization and a fast finite-difference time-domain forward operator is presented. Using data from a synthetic hydrocarbon reservoir, we demonstrate that models are reproduced with a spatial resolution determined by the skin depth of the frequencies included in the inversion. Both single and multiple resistive bodies can be resolved in the subsurface. Using reciprocal treatment and multiple frequencies at each receiver position, the comprehensive inversion sequence of a typical MCSEM survey, which should match the acquired data to within the measurement error, executes within ~100 iterations, with about 30 iterations per day, requiring at most a few hundred nodes on a parallel cluster. Introduction MCSEM surveys have been used as geophysical survey tools for several decades (e.g., Spies et al., 1980) and were revived after the pioneering studies for hydrocarbon exploration using Seabed Logging in this decade (e.g., Eidesmo et al., 2002). Continued evolution in operational accuracy and equipment have resulted in a vast improvement in data quality (see examples shown in Zach et al., 2008), which will allow the accurate measurement of both magnitude and phase for complex survey designs with arbitrary source-receiver orientations. This will further drive the development and application of 3D inversion algorithms. The inverse CSEM problem has been the subject of a number of studies. Newman and Alumbaugh (1996, 1999) used a finite-difference frequency-domain solver for a wide frequency band on a staggered grid, in an inversion scheme based on conjugate gradient update steps; see also the implementation study by Commer et al. (2008). Mackie and Watts (2007) and Bornatici et al. (2007) also use a staggered grid, finite-difference frequency-domain solver (e.g., Mackie, Madden, Wannamaker, 1993) in a preconditioned conjugate gradient loop for the joint inversion of both marine CSEM and MT data. 3D inversion based on integral equation Maxwell solvers is the subject of several studies from the University of Utah, where the MCSEM inverse problem is again formulated in regularized conjugate gradient steps, see Gribenko, Zhdanov, 2007. An efficient approach to 3D MCSEM inversion with a finite-volume forward solver, where the gradient is computed using the adjoint state method, was found by Plessix, 2006, and applied in Plessix, van der Sman, 2007. There, a quasi-Newton inversion scheme is used with a diagonal approximation to the inverse Hessian matrix. The forward solutions to all aforementioned approaches rest on the solution of Maxwell’s equations in the frequency domain, which requires separate solutions for each frequency mode in the source spectrum. With increasingly wide and complex frequency spectra in MCSEM surveys (Mittet, Schaug-Pettersen, 2007), this comprises a considerable numerical challenge. We present a method which is based on the finite-difference timedomain solution developed by Maaø, 2007, permitting the forward-solution of multiple frequencies simultaneously. Following Støren et al. (2008), the gradients are calculated from the difference field between real and synthetic data using the first Born-approximation. A quasi-Newton update is used for the optimization, where the Hessian matrix is approximated with BFGS matrices using the gradient projection method (Zhu et al., 1997, Byrd et al., 1995).

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2007 SEG Annual Meeting, September 23–28, 2007

Paper Number: SEG-2007-0579

... and Arsenin, 1977) support stabilizer stabilizer mcsem data reservoir characterization

**marine****csem****data**upstream oil & gas minimum vertical support stabilizer resistivity inversion interpretation zhdanov experiment petroleum reservoir vertical support stabilizer transmitter...
Abstract

SUMMARY Marine controlled-source electromagnetic (MCSEM) surveys have become an important part of off-shore petroleum exploration. In this paper we discuss new advances in the development of 3D inversion methods for the interpretation of MCSEM data. Our method is based on rigorous integral equation (IE) forward modeling and a new IE representation of the sensitivity (Fréchet derivative matrix) of observed data to variations in sea-bottom conductivity. We use quasi-analytical approximation for models with variable background conductivity (QAVB) for more efficient Fréchet derivative calculations. In our regularized focusing inversion algorithm we introduce a new stabilizing functional, a minimum vertical support stabilizer. This stabilizer helps generate a focused image of the relatively thin and flat resistive structure of a hydrocarbon (HC) reservoir. The methodology is tested on a 3D inversion of the synthetic EM data and the interpretation of an MCSEM survey conducted in the Troll West Gas Province (TWGP). INTRODUCTION In this paper we discuss new advances in the development of 3D inversion methods for the interpretation of MCSEM data using the integral equation (IE) method. We have also developed a new form of efficient Fréchet derivative calculations based on the IE representation (Gribenko and Zhdanov, 2007). As a result, the IE inversion method requires just one forward modeling on every iteration step, which speeds up the computations and results in a relatively fast but rigorous inversion method. Another distinguished feature of our inversion method is the use of focusing regularization (Portniaguine and Zhdanov, 1999), which provides a sharp boundary image of the petroleum reservoir. In the current paper we extend this approach by introducing a new stabilizing functional, a minimum vertical support stabilizer. This stabilizer helps generate a focused image of the relatively thin and flat resistive structure of a hydrocarbon (HC) reservoir. This new type of focusing inversion is tested on synthetic models of an HC reservoir. We also apply this new technique to the interpretation of an MCSEM survey conducted in the Troll West Gas Province (TWGP), offshore Norway. PRINCIPLES OF THE 3D REGULARIZED FOCUSING INVERSION OF MCSEM DATA The Tikhonov parametric functional with focusing stabilizers A typical MCSEM survey consists of a set of sea-bottom electrical and magnetic receivers and a horizontal electric dipole transmitter towing at some elevation above the sea bottom. The transmitter generates a frequency domain EM field. The main goal of MCSEM data interpretation is to determine the anomalous conductivity distribution, Ds, within the sea-bottom geological formations, where Ds is the difference between the total conductivity, s, and some known background conductivity, sb: Ds = s - s b . Mathematically, we can represent the corresponding EM inverse problem in the form of the operator equation: d = A(Ds) , where A is a forward modeling operator, d stands for the observed EM data in the sea-bottom receivers, and Ds is the anomalous conductivity within the targeted domain. Equation (1) describes an ill-posed inverse problem. The regularized solution of this problem can be obtained by minimization of the corresponding Tikhonov parametric functional, Pa (Ds) (Tikhonov and Arsenin, 1977)

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2007 SEG Annual Meeting, September 23–28, 2007

Paper Number: SEG-2007-0524

... survey interpretation. The 1D inversion of

**marine****CSEM****data**is an easy way to generate depth-resistivity profiles required by more advanced data processing and interpretation schemes. Examples are the generation of starting models for rigorous 3D inversion or the interpretation of Scanning (i.e...
Abstract

Introduction Summary We demonstrate how processing data from shallow water CSEM surveys using up-down separation can improve the performance of a global inversion scheme. Data from a receiver over a known prospect produces a markedly improved reproduction of the resistivity profile in a planelayer model employed for illustrative purposes. This improvement being particularly pronounced in the absence of a strong resistive anomaly, the results are directly applicable to finding starting models for more rigorous 3D inversion schemes as well as a to creating a reference model for Scanning survey interpretation. The 1D inversion of marine CSEM data is an easy way to generate depth-resistivity profiles required by more advanced data processing and interpretation schemes. Examples are the generation of starting models for rigorous 3D inversion or the interpretation of Scanning (i.e. reconnaissance) survey data (Wahrmund et al., 2006). The inversion of marine CSEM data is inherently illconditioned, in particular in shallow water (water depth <500m), where the strong air wave dominates the measured electromagnetic field at large source-receiver offsets, thus masking the response from deeper resistors/hydrocarbon reservoirs (Roth and Maaø, 2007). Amundsen et al. (2006) introduced an effective method which attenuates the air wave and increases the sensitivity of marine CSEM methods by separating the measured wavefield into its upand downward traveling constituents. Here we present example results from inverting shallow water CSEM data acquired offshore Norway using a simple 1D inversion scheme that combines the sensitivity enhancement of updown wavefield separation with the global optimization capabilities of a simulated annealing (SA) search algorithm. Simulating annealing Up-down separation for air wave attenuation Air wave attenuation by up-down separation takes advantage of the fact that the information about the subsurface is contained in the upward traveling constituent of the wavefield in the seafloor just below the CSEM receiver, whereas the air wave is traveling downward. Assuming a primarily vertically traveling wavefield, as is the case for large source-receiver offsets, the separation can be applied on a receiver-by-receiver basis using a simple linear combination of the measured electric and magnetic field components. Here, the superscript ( U ) denotes “upward”, ? SF is the resistivity of the seafloor, ? is angular frequency, _ denotes the magnetic permeability. Similar expressions exist for the upward constituents of E y and H x , respectively. The seafloor resistivity needs to be known a priori, however Roth and Maaø (2007) showed that the decomposition relation (1) is well-behaved and tends to enhance the sensitivity to resistive subsurface structures even when the assumed resistivity is incorrect. We therefore propose to use the same seafloor resistivity in the up-down separation as in the top-most layer of the inversion model. This approach renders the problem more non-linear as compared to keeping the resistivity fixed a priori, which favors the use of a global inversion scheme such as SA over gradient-based methods. The technique of simulated annealing (SA) was invented in the early-mid 1980’s, based on the Metropolis algorithm, and has since become a tool in most fields of computational optimization.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2007 SEG Annual Meeting, September 23–28, 2007

Paper Number: SEG-2007-0633

... Summary We have studied the EM field distribution in anisotropic media and the impact of resistivity anisotropy on

**marine****CSEM****data**. The effects of anisotropy may be significant for typical acquisition geometries and depend upon the transmitter-receiver geometry. We also show that different...
Abstract

Summary We have studied the EM field distribution in anisotropic media and the impact of resistivity anisotropy on marine CSEM data. The effects of anisotropy may be significant for typical acquisition geometries and depend upon the transmitter-receiver geometry. We also show that different data components are affected differently. In general, the anisotropy of the overburden must be determined in order to make a reliable CSEM data interpretation. Introduction The marine CSEM method is increasingly being used to evaluate reservoir fluid type (Ellingsrud et al. , 2002; Srnka et al. , 2005; Smit et al. , 2006). The resistivity difference between hydrocarbon-filled and water-filled reservoirs can be up to two orders of magnitude. The marine CSEM method exploits this difference to predict the type of reservoir fluid and thus reduce drilling errors. It is estimated that hundreds of marine CSEM surveys have been acquired worldwide in different geologic settings. A typical survey consists of a number of source towlines crossing over the target. Multi-component receivers are deployed along and/or off these towlines. Measured data are processed and interpreted using inversion and modeldriven interpretation to map sub-seafloor resistivity variations for exploration, development, and production. Other geophysical data, such as seismic and well logs, are often available in the area where a marine CSEM survey is being conducted. These data provide constraints on CSEM data interpretation. In analyzing field data, we have observed that the background resistivity obtained from CSEM data could be up to three times larger than that obtained from wireline logs in some basins. In some cases, the background resistivity determined from receivers on the towline is much higher than that from broadside receivers. These differences may be caused by electrical anisotropy. In sedimentary basins relevant to hydrocarbon exploration, thinly laminated sequences of shale and sand are commonly believed to produce electrical anisotropy. Electrical anisotropy has been well studied in resistivity logging (e.g., Lu and Alumbaugh, 2001; Yu et al. , 2001). However, the effect of electrical anisotropy on marine CSEM data is less well known (Tompkins, 2004). In this paper, we compute the electric field distribution in anisotropic media and investigate the effects of anisotropy on measurements made on the seafloor, and discuss the dependence of the anisotropic effect on transmitter-receiver geometry. We also show that each data component is affected differently. Effects of Anisotropy on EM Fields Marine CSEM data are often presented in amplitude and phase. Both amplitude and phase are affected by the anisotropy of the sub-seafloor, but we present only amplitude results here. Also, only results in the first quadrant are shown, and the results in the other three quadrants can be obtained from symmetry arguments. Five models are considered in this section by varying the resistivities of the lower half-space: 1) ?v = ?h = 1.0 O·m; 2) ?v = 2.0 O·m, ?h = 1.0 O·m; 3) ?v = 2.0 O·m, ?h = 2.0 O·m; 4) ?v = 3.0 O·m, ?h = 1.0 O·m; and 5) ?v = 3.0 O·m, ?h = 3.0 O·m.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2006 SEG Annual Meeting, October 1–6, 2006

Paper Number: SEG-2006-0815

...

**marine****csem****data**receiver conductivity Rigorous 3-D inversion of**marine****CSEM****data**based on the integral equation method Alexander Gribenko , and Michael Zhdanov, University of Utah SUMMARY Marine controlled source electromagnetic (MCSEM) surveys have be- come an important part of offshore...
Abstract

SUMMARY Marine controlled source electromagnetic (MCSEM) surveys have become an important part of offshore petroleum exploration. In this paper we present a new approach to 3-D inversion of MCSEM data. It is based on rigorous integral equation (IE) forward modeling and a new IE representation of the sensitivity (Frechet derivative matrix) of observed data to variations in sea-bottom conductivity. This approach requires just one forward modeling on every iteration of the regularized gradient type inversion algorithm, which speeds up the computations significantly. We also use a regularized focusing inversion method, which provides a sharp boundary image of the petroleum reservoir. The methodology is tested on a 3-D inversion of the synthetic EM data representing typical MCSEM surveys conducted for offshore petroleum exploration. INTRODUCTION During recent years marine controlled source electromagnetic (MCSEM) surveys have become intensively used for off-shore petroleum exploration (Eidesmo et al., 2002; Ellingsrud et al., 2002; Carazzone et al., 2005; Hesthammer and Boulaenko, 2005). The success of the EM method''s application for the search of oil and gas reservoirs is based on the fundamental fact that oil- and gas- containing structures are characterized by very high resistivity, while the surrounding sea-bottom formations filled with salt water are very conductive. Therefore, a petroleum reservoir represents a clear target for EM methods. However, the interpretation of MCSEM data is still a very challenging problem, especially if one would like to take into account a real three-dimensional (3-D) structure of a sea-bottom geological formation. In this paper we present a new method of 3-D inversion of MCSEM data which uses a rigorous integral equation (IE) based forward modeling and regularized focusing inversion algorithm. To obtain a stable solution of a 3-D inverse problem we apply a regularization method based on a focusing stabilizing functional (Zhdanov, 2002). This stabilizer helps generate a sharp and focused image of anomalous conductivity distribution, which is important in petroleum exploration with the goal of delineating the boundaries of the prospective reservoir. We present in this paper the results of the application of the rigorous inversion method to the inversion of the synthetic MCSEM data. IE METHOD IN 3-D INVERSION OF MCSEM DATA We consider, first, the typical MCSEM survey consisting of a set of sea-bottom electrical and magnetic receivers and a horizontal electric dipole transmitter towing at some elevation above the sea bottom. The transmitter generates a frequency domain EM field. The operating frequencies are usually selected to be low enough (in a range of 0.1 - 5 Hz) to propagate through the conductive sea water and sea-bottom layers of the sediments and to illuminate the sea-bottom geological structures. The field recorded by the receivers can be represented as a sum of the normal EM field,{E norm , H norm }, generated in the horizontally layered background model formed by the sea water and the sedimental layers, and an anomalous part, { E a , H a }, related to the horizontal conductivity inhomogeneities, Ds present in the sea bottom. The anomalous electric field is related to the electric current induced in the inhomogeneity, j= Ds E

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2006 SEG Annual Meeting, October 1–6, 2006

Paper Number: SEG-2006-0704

... Summary

**Marine****CSEM****data**processing techniques have evolved rapidly to accommodate the proliferation of marine controlled-source electromagnetic (CSEM) surveys. Processing methods developed for marine MT surveys are not directly applicable to**marine****CSEM****data**. Basic**marine****CSEM****data**processing...
Abstract

Summary Marine CSEM data processing techniques have evolved rapidly to accommodate the proliferation of marine controlled-source electromagnetic (CSEM) surveys. Processing methods developed for marine MT surveys are not directly applicable to marine CSEM data. Basic marine CSEM data processing eliminates the signature of the acquisition system and extracts the normalized spectral response of the earth from the receiver time series data. More sophisticated methods have been developed to simplify interpretation and to ensure that the highestquality data are available for inversion. These methods include determining the 3D receiver orientations by inversion, extending the range of useful offsets by noise suppression, and minimizing the effect of air waves by model-based subtraction. Introduction Marine CSEM exploration is a direct hydrocarbon detection technology that exploits the resistivity contrast between hydrocarbon reservoirs and the surrounding sediments. Since the earliest commercial surveys in the West Africa offshore (Ellingsrud et al , 2002; Srnka et al , 2005), acquisition, processing, and interpretation methods have evolved rapidly. These surveys use receivers deployed on the seafloor and a deep-towed source. The measured fields are analyzed to investigate sub-seafloor structures and determine the nature of the targeted reservoir. Typically, surveys have a number of towlines crossing over the target and receivers positioned primarily along those towlines. The source is towed about 50 meters above the seafloor while all receivers are recording. After completing the towlines, receivers are recovered and the recorded data are downloaded so the data can be processed and interpreted offline. The land counterpart, controlled source audio-frequency magnetotellurics (CSAMT), has been used since its introduction in the mid-1970s (Goldstein and Strangway, 1975). Since CSAMT surveys typically measure both electric and magnetic fields, data processing mimics standard MT processing techniques (Egbert and Booker, 1986). In marine CSEM surveys, it is often the case that only electric fields are measured. As a result, MT techniques cannot be directly applied. Also, new challenges and problems have arisen in the marine environment. In this paper, we discuss the basics of marine CSEM data processing and techniques for orienting the receivers, reducing background noise, and suppressing air waves. Basic Steps of Data Processing The essence of data processing is to transform the receiver, transmitter, and navigation data into an interpretable form. Receiver fields, source current, and navigation data are all recorded in the time domain. As interpretation is currently done in the frequency domain, the amplitude and phase of the EM response are required. Basic processing steps include spectral decomposition (transforming data from time to frequency domain), receiver and source normalization, and source-receiver geometry construction (merging navigation data with processed EM fields): • Spectral Decomposition : For spectral decomposition, the time series data are binned and each bin is tagged with a time corresponding to its center. Spectral decomposition on each bin could be done with the Fourier transform. However, since the source transmits only a small number of pre-selected frequencies, the amplitude and phase at those frequencies are computed more efficiently by a least-squares fit.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2006 SEG Annual Meeting, October 1–6, 2006

Paper Number: SEG-2006-0719

... and lithologically complex environments (Carazzone et al., 2005). The tool set most commonly available to interpreters includes one-, two- and three-dimensional forward and inverse modeling codes. All previous examples, reported in the literature, of inversion codes applied to

**marine****CSEM****data**have been cell-based...
Abstract

Summary The use of controlled source electromagnetics (CSEM) in the marine environment has grown rapidly in the past few years from a simple anomaly fluid-hunting technique used in geologically simple environments to a modeling and inversion based technique applied in structurally and lithologically complex environments (Carazzone et al., 2005). The tool set most commonly available to interpreters includes one-, two- and three-dimensional forward and inverse modeling codes. All previous examples, reported in the literature, of inversion codes applied to marine CSEM data have been cell-based regularized techniques designed to produce the smoothest possible isotropic conductivity model (in two- or three-dimensions) which fits the observed data. We report on the development of a new technique, anisotropic sharp-boundary inversion in which the model is parameterized by two-dimensional interfaces. In this approach anisotropic conductivity can have sharp contrasts across interfaces. Regularization is applied to the smoothness of the interface and the lateral variations of conductivity between interfaces. We demonstrate a work flow that progresses from forward modeling through fast depth migration to smooth cell based inversion, concluding with sharp boundary inversion for the final interpreted conductivity image. Introduction Interpretation of marine CSEM data in complex environments requires a combination of forward modeling and inversion in order to assess the impacts of different elements of the geology on the observed CSEM signal. CSEM data used for hydrocarbon exploration are commonly called seabed logging (SBL) by the industry. The interpretation of SBL data, in conjunction with seismic and log data, from the gas portion of the Troll field and a current exploration target in the North Sea is presented. In all cases gas or oil accumulation in the section causes a portion of the section to be electrically resistive. Assessment of the CSEM signal from any hydrocarbon accumulation can be complicated by the presence of thin calcite strings, possible significant electrical anisotropy as well as possible anhydrates and salt. Figure 1 shows the seismic section across a portion of the Troll field in the gas province. A gas-oil contact has been identified at approximately 1600 m. The oil leg in this location is less then 10 m and does not provide a significant CSEM signal. Directly below the thin oil zone, the remainder of the high porosity sands are brine filled. Method Forward modeling is required to determine the frequency, offset-range, and magnitude of signals produced from non-target elements such as nearby anhydrates, thin calcite stringers or even bathymetry. Once forward modeling has established that the potential target reservoir should produce a response which can be distinguished above the response of all other potential “geologic noise”, inversion is used to produce conductivity images which are consistent with the observed data. Conductivity images produced by smooth cell based inversion (SCBI) are not however always consistent with other geophysical data, primarily reflection seismic, which provides subsurface images of interfaces between geologic units. An inversion scheme based on interfaces is inherently more consistent with seismic imaging and can readily take advantage of structural control provided by seismic images.