Error propagation and model update analysis in three-dimensional CSEM inversion

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).

Integrated analysis of well, seismic and controlled source electromagnetic (CSEM) data is key in order to provide valuable information on reservoir properties and content. The purpose of this study was to jointly analyze well data, seismic data and CSEM data to provide a clear appraisal of a prospect offshore West Africa. Firstly, CSEM data was modeled and inverted. Results were then integrated with the available seismic and well data in order to better understand the resistivity changes we were seeing in the CSEM inversion. In particular, the observation of a low resistivity zone coincident with the seismically mapped prospect is disappointing; however two plausible geological explanations were considered and led to the decision of dropping the block.

An extensive marine controlled source electromagnetic (CSEM) data set was collected in the Gulf of Mexico targeting gas hydrate deposits at four geologically distinct areas within water depths that varied from 900 to 3000 m. The Joint Industry Project 2 drilled two of these locations, Green Canyon 955 and Walker Ridge 313, providing ground truth for the CSEM data. A third survey location, Mississippi Canyon 118 (MC118), is a designated hydrate observatory. We collected both traditional ocean bottom electromagnetic receiver data as well as continuous profiling data using a novel towed three-axis electric field receiver. The towed receiver measured data at a fixed offset of 300 m behind the transmitter, while the ocean bottom receivers recorded data over a range of offsets as the transmitter passed by each receiver. We inverted the CSEM data using a newly developed parallel goal-oriented adaptive finite element-modeling algorithm for efficient 2.5D imaging of the CSEM data. 2D inversions from MC118 are consistent with 1D apparent resistivity pseudosections and reveal resistive areas associated with the carbonate/hydrate mound. An extensive conductive region exists below this, associated with conductive brines sourced from a deeper more resistive salt body. Away from the mound the resistivity structure is a fairly uniform and homogeneous. We present new 2D inversions of the CSEM data that consider electrical anisotropy and constrained inversions that leverage structural control using key seismic horizons along the profiles.

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.

It is well known that no single geophysical method can provide a completepicture of the earth and its properties and recently integrated interpretationand joint inversion of multiple data types has become a much studied topic(Chen et al, 2007; Harris et al, 2009; Jegen et al, 2009; de Stephano et al,2011). The seismic method is in many situations the tool of choice: it isgeneral and widely applicable, and can provide detailed images of sub-surfacestructure and stratigraphy from which complex geological models can beconstructed. If seismic methods can provide the answer to the question ofinterest then this is undoubtedly the tool to use. However although seismicdata are extremely sensitive to the changes in lithology occurring at theboundaries between geological units, they are less sensitive to fluid changeswithin these units. This is because acoustic and elastic properties of theearth show only small changes when the fluid content or saturation is changed. These changes can in some circumstances be detected and used to provideinformation on fluid distribution. In other situations this is difficult orimpossible to do with certainty, and complementary geophysical methods must beemployed to meet the reservoir characterization goal. In many situations electrical resistivity is driven by the properties anddistribution of fluids in the earth. Commercial hydrocarbon deposits may bemany times more resistive than surrounding lithologies. This change inresistivity caused by variations in fluid content and saturation can, inprinciple, be detected using CSEM tools (See Constable & Srnka 2007 for areview of CSEM technology). However when only CSEM data are considered, structural resolution is poor because of the diffusive nature of the EM fields, and the results can be ambiguous because the effect of an increase in porefluid resistivity cannot be distinguished from the effect of a decrease inporosity. The presence of frustrating resistors in the section (for exampletight carbonates, cemented sandstones or volcanics) can also complicate theinterpretation. Well-log data provide a range of measurements, including both resistivity andacoustic/elastic properties as well as a range of further properties. Apetrophysicist analyzing this well log data will take all of these measurementsand integrate them together to provide an interpretation of the lithology andfluid properties. The resistivity measurement in particular provides keyinformation on the fluid content at the well bore. However such informationcannot provide any constraint on the variation of properties away from thewell, across a reservoir. For any given geophysical question, the most robust answer will be obtained byusing the tool, or combination of tools best suited to the task, andintegrating the resulting data within a rock physics framework, to provide ashared earth model that is geologically reasonable, and consistent with each ofthe geophysical data types available. Careful integration of multiple datatypes can allow the strengths in one method to compensate for the weaknesses inanother. Here we consider three data types: seismic, controlled source electromagnetic(CSEM), and well log data. The workflow applied is shown in figure 1. CSEM dataare first inverted to provide a measure of resistivity. Because of the poorstructural resolution of CSEM data taken in isolation, this inversion isconditioned with seismic structural information. From the results, thetransverse resistance (the vertically integrated resistivity), which is wellconstrained by the CSEM method, can be calculated within the intervals ofinterest.

In August 2008, a marine Controlled Source ElectroMagnetic (CSEM) survey was acquired by WesternGeco EM for the previous operator (Encana) of Lady Franklin and Atammik blocks offshore west Greenland. The objective of the project was to rank possible hydrocarbon prospects in the upper Cretaceous section. Therefore, the survey was designed to optimize CSEM responses based on the previous operator's interpretation within a limited budget, resulting in a pseudo-3D survey, sub-optimal to assess actual interpretation updated according to the results of the three exploration wells drilled by Cairn in the block during 2011. Independent 3D inversions of the CSEM data run before the drilling campaign suggest a consistent background resistivity over the different prospects. Drilling results confirmed the resistivity distribution as recovered by 3D CSEM inversions within the data resolution limit. After the drilling campaign, a sensitivity study was carried out in order to assess the limit at which the geological problem can be tuned by the electromagnetic data response. This showed that the resistive anomalies visible on some of the prospects in the block cannot be assessed for hydrocarbon saturation on the basis of electromagnetic data alone as they are below the resolution of the method given the non-uniqueness of the results. The present paper highlights how non-seismic data, such as electromagnetic, can provide additional information which, integrated with other geophysical and geological data, can help exploration during the different phases. The pre-drilling analysis of the resistivity distribution as recovered by 3D inversion of the data identified resistive anomalies that were interpreted as hydrocarbon indicators. The drilling results identified the geological meaning of these anomalies, which were further analyzed to include and extrapolate the new post-drilling interpretation in order to evaluate the non-drilled prospects. The sensitivity study was critical to understand the EM data limitations in this geological context before investing in a new project. Introduction Electromagnetic data are widely applied in the oil industry in order to de-risk exploration, reducing the uncertainty related to reservoir saturation (Hesthammer et al., 2012). Many success stories increased the confidence in electromagnetic data as hydrocarbon detectors: in fact, electromagnetic data provide information exclusively regarding the resistivity distribution of the underlying earth. Resistive anomalies may be related to fluid saturation, but they may also be produced by different geological features not necessarily related to fluid saturation: volcanics, carbonates, and evaporites are only some examples of lithologies characterized by high resistivity values. In addition, inversion of electromagnetic data is a non-unique problem and therefore constraining the inversion with the available geological and geophysical information is critical in order to produce a geologically meaningful model and subsequent interpretations. The limited data and frequency range (~ 0.1 to 10 Hz), the diffusive physics of CSEM, and the necessity (usually) of using a smooth stabilizer for inversion result in low resolution.

The Controlled-Source Electromagnetic (CSEM) method is one of the electromagnetic methods utilized in geophysical exploration. This method provides a subsurface image through the resistivity anomalies of materials encountered by electromagnetic waves. The research area is located near a major city, resulting in high electromagnetic noise. Electromagnetic noise can be categorized into two types of the noise namely periodic noise and sporadic noise. Eliminating noise is a crucial objective to enhance data quality, as it can introduce uncertainty into interpretations. Three noise removal techniques are employed: pre-stack to filter the harmonic noise, stacking to remove the sporadic noise, and post-stack for smoothing. The CSEM data used consists of signals in the time domain with a 10-second period and a 50% duty cycle. The results of applying these noise removal techniques indicate that all three methods are highly effective in noise reduction. The pre-stack technique can remove periodic noise, while sporadic noise is addressed by the stacking technique, and signal smoothing can be achieved using the poststack technique.

Porosity and free gas estimates from controlled source electromagnetic data at the Scanner Pockmark in the North Sea

Michael S. Zhdanov, Evgeny P. Velikhov, Martin Čuma, Glenn Wilson, Noel Black and Alexander Gribenko argue that the key to deriving a reliable quantitative interpretationfrom marine controlled-source electromagnetic (CSEM) data is through the integration of shared earth modelling and robust 3D CSEM inversion. An iterative migration method for CSEM data is presented that is equivalent to rigorous inversion, illustrated with a case study of the Shtokman gas field in the Barents Sea.

Reservoir characterization objectives are to understand the reservoir rocks and fluids through accurate measurements to help asset teams develop optimal production decisions. Within this framework, we develop a new workflow to perform petrophysical joint inversion (PJI) of seismic and controlled-source electromagnetic (CSEM) data to resolve for reservoirs properties. Our workflow uses the complementary information contained in seismic, CSEM, and well-log data to improve the reservoir’s description drastically. The advent of CSEM, measuring resistivity, brought the possibility of integrating multiphysics data within the characterization workflow, and it has the potential to significantly enhance the accuracy at which reservoir properties and saturation, in particular, can be determined. We determine the power of PJI in the retrieval of reservoir parameters through a case study, based on a deepwater oil field offshore Brazil in the Sergipe-Alagoas Basin, to augment the certainty with which reservoir lithology and fluid properties are constrained.

One of the major problems in the modeling and inversion of marine controlled-source electromagnetic (CSEM) data is related to the need for accurate representation of very complex geoelectrical models typical for marine environment. At the same time, the corresponding forward-modeling algorithms should be powerful and fast enough to be suitable for repeated use in hundreds of iterations of the inversion and for multiple transmitter/receiver positions. To this end, we have developed a novel 3D modeling and inversion approach, which combines the advantages of the finite-difference (FD) and integral-equation (IE) methods. In the framework of this approach, we have solved Maxwell’s equations for anomalous electric fields using the FD approximation on a staggered grid. Once the unknown electric fields in the computation domain of the FD method are computed, the electric and magnetic fields at the receivers are calculated using the IE method with the corresponding Green’s tensor for the background conductivity model. This approach makes it possible to compute the fields at the receivers accurately without the need of very fine FD discretization in the vicinity of the receivers and sources and without the need for numerical differentiation and interpolation. We have also developed an algorithm for 3D inversion based on the hybrid FD-IE method. In the case of the marine CSEM problem with multiple transmitters and receivers, the forward modeling and the Fréchet derivative calculations are very time consuming and require using large memory to store the intermediate results. To overcome those problems, we have applied the moving sensitivity domain approach to our inversion. A case study for the 3D inversion of towed streamer EM data collected by PGS over the Troll field in the North Sea demonstrated the effectiveness of the developed hybrid method.

To improve the interpretation of controlled‐source electromagnetic (CSEM) data it is desirable to include information from seismic data in a joint interpretation. The higher resolution of the seismic image makes it possible to accurately determine the depth of resistivity contrasts detected by the CSEM data. In this paper we show how horizons derived from seismic data can be incorporated in a CSEM inversion workflow to obtain resistivity models which are more consistent with both the CSEM data and the seismic data, and we demonstrate this workflow on a CSEM field data set.

We present results from an investigation into 1D inversion of controlled-source electromagnetic (CSEM) data. Based on inspection of a data set, we formulate a simple empirical noise model described by a few pragmatically determined parameters. We also investigate the effects of transmitter height above the seafloor and include the data uncertainty resulting from varying transmitter height in our noise model. Based on the noise model and assumptions about the available data, we analyze model parameter uncertainty estimates derived from the a posteriori model covariance matrix for a resistive layer buried at depth. We find that the layer parameters uncertainty primarily depends on the depth of burial and the thickness of the layer. We then formulate quantitative bounds for these parameters, within which we have a small uncertainty of the parameters of the resistive layer. The depth of burial and the transverse resistance of the layer become better determined the higher its resistance. We invert a field data set with multilayer and four-layer models and find a resistive layer at a depth of 1500–1600m below the seafloor. Seismic results from the area indicate a depth of 2000m.

Discussed here are the results of a 1D and 3D forward with 1D and 2D inverse Controlled Source Electromagnetics (CSEM) modeling exercise over a know target offshore West Africa with the intent of demonstrating the technology, develop methodology for analysis and better understanding its limitations. The 3D dataset presented here is considered rich consisting of 11 transmitter (Tx) tow lines and 43 electric and magnetic seafloor receivers (Rx) arranged in a 3D grid pattern (see Figure 1) and acquired with a time‐shared dual base frequency square waveform of 0.25 and 0.0625 Hz. Aspects of detectability, comparison to modeled results (1D, 2D and 3D), and resolution of known structure both horizontal and vertical, comparison with logged resistivities in a constrained and unconstrained sense are analyzed. For example — detection of the target structure seems apparent in the unconstrained 2D inversions of the CSEM data. However, this inversion has reduced ability to image separate, vertically stacked pay and laterally non‐extensive bodies in the unconstrained inversions and forward modeled data. Contrast will also be made between the 1D and 2D inversions to assess the limits in imaging the hydrocarbon filled structures, along with 3D forward calculated responses to be compared with the acquired data. Combined and joint inversion of the CSEM and Marine MagnetoTelluric (MMT) information will also be considered in order to better constrain the deeper resistivity section not sampled by well logs and to improve imaging of the 3D complexity of our known target.

The Marine Electromagnetic method has routinely been used for hydrocarbon exploration for about a decade, but has not yet found its way into the mainstream workflow of most exploration companies. In this paper we will focus on some important aspects in the handling of controlled source electromagnetic (CSEM) data, concerning inversion, interpretation and risk assessment. The workflow is illustrated with a case example from the Nordland Ridge in the Norwegian Sea. The CSEM and marine magnetotelluric (MMT) data from this area were acquired through the EDDA consortium in 2010.