Direct elastic properties to resistivity transforms

A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO2 monitoring, the first step is done with either controlled source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A model-based parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO2 sequestration in the Skade formation. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

Since the late 1990s, the continuous development and application of the Controlled-Source Electromagnetic (CSEM) technology have vastly improved its usage in exploration (e.g., Constable, 2010; Loseth et al., 2015; Zach et al., 2009). CSEM inversion results are the main input for interpretation of resistivity data, especially potential fluid anomalies. Complementary to other geophysical methods, e.g. Amplitude versus Offset (AVO), it can therefore be a valuable tool in hydrocarbon exploration. Yet, the unconstrained nature of the subsurface and non-unique¬ness of inversions is a tough predicament. For example, several features in the subsurface could cause high resistivity, such as abundant limestone, salt, basalt, mature source rocks or hydro¬carbon-filled sandstones (Baltar and Barker, 2015). As a result of enhanced data quality, data processing and inversion capabilities, together with improved workflows and accumulated experience, CSEM interpretation has become increasingly more robust (Buland et al., 2011; Karman et al., 2013; Loseth et al., 2014). The 3D Gauss-Newton (GN) inversion algorithm is a more robust method compared to other inversion types. GN thus provides a new, powerful methodology for generating marine CSEM resistivity images. Nonetheless, it has formerly been com¬putationally exhaustive to apply. With the Hoop area, Barents Sea, recently being nominated for the Barents Sea exploration licensing round, a tight deadline was given for the CSEM inversion of an expansive 11,000 km2, over 130 blocks (refer to Figure 1). Adopt¬ing the flexible and agile cloud system into the CSEM workflow, it has improved the 3D GN work efficiency and provided overall consistent results when compared with other CSEM inversion schemes. This paper will illustrate how a systemic workflow of CSEM 3D GN inversions together with efficient cloud computing, could lead to a more consistent interpretation of this data.

In controlled-source electromagnetic (CSEM) inversion with conventional regularization, the reconstructed conductivity image is usually blurry and only has limited resolution. To effectively obtain more compact conductivity models, we apply the concept of multinary transformation to CSEM inversion based on the finite-element method with an unstructured tetrahedral mesh. Within the framework of multinary inversion, the model conductivities are allowed to be clustered only within the designed values, which are usually obtained from other a priori information or from conventional inversion. Synthetic studies show that multinary inversion produces conductivity images with clearer model boundaries compared to the maximum smoothness inversion and the focusing inversion for realistic geoelectric models. We further apply the developed method to a land CSEM survey for mineral exploration. The multinary inversion results are closer to the ground truth compared to the conventional maximum smoothness inversion and the focusing inversion. The developed method and the numerical algorithm provide a new approach and workflow for CSEM inversion when the models need to have clear boundaries and clustering model values. Such geoelectric models could be very useful for geologic interpretation in oil and mineral exploration when a priori information (such as the estimated conductivity values) of the exploration targets is known.

Gas hydrates are a potential energy resource and hazard for drilling and infrastructure, yet estimates of global volume vary by over three orders of magnitude. Hydrates are electrically resistive compared to water saturated sediment and so electromagnetic methods provide an additional tool to seismic surveys and drilling for determining hydrate saturations. A marine electromagnetic survey was carried out at Hydrate Ridge, Oregon, USA, with the aim of testing the use of controlled source electromagnetic (CSEM) and magnetotelluric (MT) methods to map gas hydrate and free gas below the gas hydrate stability zone. A 2-D CSEM inversion supports the scenario deduced from previous seismic and drilling results, which indicate two mechanisms of hydrate emplacement: a transport-dominated and reaction-dominated regime. A prominent resistive region of 2.5–4 ?m at a depth of about 130 mbsf, near the seismic bottom simulating reflector (BSR), suggests that 27 to 46 per cent of the bulk volume is filled with hydrate, depending on whether Archie's Law or the Hashin-Strikman bounds are used. This is representative of a reaction-dominated regime for hydrate emplacement, and where a significant low velocity zone exists based on a seismic tomography inversion, suggests large quantities of free gas below the BSR. Electrical resistivity logging while drilling (LWD) data show general agreement with the CSEM inversion model except for a CSEM-derived resistive region at seismic horizon A, known to transport free gas into the gas hydrate stability zone. Inversion of MT data collected simultaneously during the CSEM survey provides a complimentary low-resolution image of the shallow sediments and shows folding in the accretionary complex sediments similar to that imaged by a tomographic seismic velocity model.

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.

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.

Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam’s inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam’s inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.

SUMMARY This study examines error propagation from data to model space during 3-D inversion of controlled-source electromagnetic (CSEM) data using a Gauss–Newton-based algorithm. An expression for model parameter correction is obtained using higher order generalized singular value decomposition for various regularization strategies. Inverse modelling is performed for different types of noise employing distinct regularization schemes to investigate the impact of error. Data corrupted with random noise suggest that the random noise mainly propagates when regularization parameters are small, owing to the high-frequency nature of random noise. Furthermore, the random noise predominantly causes artefacts in the shallower part of the inverted model. However, it has little impact on the estimation of major anomalies because the anomaly primarily depends on the smoothly varying parts of data. These observations are valid for both isotropic and anisotropic inversions. Resistive geological anomalies, like vertical dyke or vertical fractures, may pose a significant challenge for isotropic inversion in terms of convergence and data fit, even if the subsurface is isotropic. On the other hand, anisotropic inversion performs remarkably well in such cases, showing faster convergence and better data fit than isotropic inversion. Anisotropic inversion is indispensable in the case of an anisotropic host medium, as isotropic inversion produces significant artefacts and poorer data fit. Numerical experiments suggest that, in general, anisotropic inversion produces relatively better data fit and faster convergence, even in the case of isotropic subsurface. However, due to the varying degree of sensitivity of CSEM data on thin resistive bodies, caution is required in interpreting an anisotropy obtained using anisotropic inversion. An investigation of field data also supports the observations obtained using synthetic experiments.

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.

libEMMI_MGFD: A program of marine controlled-source electromagnetic modelling and inversion using frequency-domain multigrid solver

A scaled experiment for the verification of the SeaBed Logging method

This paper provides an overview of several seismic inversion approaches and their application to very- and ultra-high-resolution marine seismic datasets. The importance of seismic inversion is related to the current demands of the offshore engineering industry for estimates of elastic stress-related parameters of near-surface and bottom sediments. To meet these demands, the seismic inversion methods which facilitate the estimation of elastic properties of the subsurface should be developed for very high-resolution data. Several problems prevent the successful application of seismic inversion to high-resolution datasets. Firstly, borehole measurements, which are routinely applied to constrain seismic inversion, are often not available for the offshore near-surface. Secondly, very- and ultra-high-resolution seismic data are acquired in high frequency ranges, and low-frequency information is absent in such datasets. The elaboration of seismic inversion methods for ultra-high-resolution data is important for both practical and scientific purposes. In the paper, we share our experience in applying acoustic poststack inversion and prestack AVA inversion to very- and ultra-high-resolution marine seismic datasets.

Nauli Field is one of the marginal fields that is located in the western region of Indonesia and has been proven to produce oil and gas hydrocarbons. The field is located in the South Sumatra Basin with reservoir target is sandstone from Talang Akar Formation. The purpose of this study is to integrate petrophysical interpretation and seismic data analysis. Reservoir characterization was performed based on porosity and water saturation for each layer within the wells. Seismic inversion methods chosen to estimate the attributes of P-impedance (Zp), S-impedance (Zs) and density (ρ). Amplitude Versus Offset (AVO) inversion through Lambda-Mu-Rho (LMR) attribute can provide information on lithology and fluids content in the reservoirs. The connection between seismic inversion and AVO inversion was to validate the gas content from the seismic data and its lateral dispersion. Data processing and analysis shows that the effective porosity for Nauli Field is between 10 to 20 porosity unit (p.u) with 20-70% water saturation. The thickness of net pay is 2-8 meters. Post-stack inversion shows impedance anomaly around 1580-1590 ms with value 22.000-25.000 (m/s)*(g/cc). Petrophysical analysis estimated this anomaly as layer-x sandstone with gas fluid saturated. LMR attribute confirm this fluid with lower Lambda-Rho values around 11-20 (Gpa)*(g/cc) than Lambda-Rho values around 28-32 (Gpa)*(g/cc).

Seismic inversion is now commonly used on post-stack and pre-stack seismic data for estimating rock properties used in reservoir characterization. While seismic migration is aimed at imaging the reflectors or the interfaces at their correct subsurface locations, seismic inversion attempts to estimate elastic and flow properties of the layers bounded by these interfaces. Seismic data are sensitive essentially to seismic wave velocity and density contrasts in the subsurface rocks. Because of significant overlap in elastic properties among different rock types, mapping of these elastic properties to rock types and estimating porosity are not trivial. Thus, seismic inversion is best done by combining data of all types such as seismic, well log, petrophysics and production history. The results of seismic inversion can be best interpreted by a team of geologists, geophysicists and reservoir engineers. The goal of this book is to explain the fundamental principles of seismic exploration, seismic wave propagation, and inversion in a language that is understandable by geoscientists and engineers alike so that the team is aware of the significance and limitations of the inversion results.