Cloud computing for large-scale controlled-source electromagnetic inversions, Barents Sea, Norway

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.

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 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.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 16879, “Mapping Hydrocarbons Integrating Seismic With Electromagnetic and Gravity Attributes: Application in the Barents Sea,” by Paolo Dell’Aversana, Stefano Colombo, Barbara Ciurlo, Johan Leutscher, and Jan Seldal, Eni, prepared for the 2013 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Electromagnetic attributes have been used extensively for interpreting marine controlled-source electromagnetic (CSEM) data. One of the basic attributes is called NAR (normalized-anomalous-amplitude response). Combining NAR and CSEM asymmetry represents a relatively simple way to use the benefits of both attributes jointly. This integrated approach, based on seismic data combined with electromagnetic and gravity attributes, has been applied in a complex exploration area in the Barents Sea. Introduction Inversion of geophysical data measured at surface represents a quantitative approach for estimating physical properties of hydrocarbon reservoirs and obtaining information about background geology (faults, structural trends, and lateral geological variations). This approach generally is based on the minimization of an objective function including the misfit between observed and predicted responses plus a regularization function. Inversion methods generally require long computation times and extensive use of parallel-computing resources. Moreover, they can produce nonunique solutions. A different but complementary approach for interpreting geophysical observations is based on the analysis of quantities, often called attributes, extracted from measurements by simple mathematical manipulation. Examples of electromagnetic attributes recently applied in hydrocarbon exploration are the gradient of CSEM magnitude vs. offset and the symmetry properties of the CSEM response itself. It has been demonstrated that CSEM attributes allow, at least in relatively simple scenarios, accurate and fast identification of reservoir boundaries. Also, in the gravity domain, attributes can work efficiently for rapid identification of important general features, such as basement trends, presence of fault systems, depth range of density anomalies, and lateral extension of salt domes and basalts. Attribute analysis cannot be considered a substitute of inversion, especially in complex geological settings, but instead should be considered as a complementary approach. In fact, in complicated scenarios, attributes can be used as a quick and efficient tool for preliminary identification of the main features of the model (resistivity and density boundaries, presence of faults, and approximate depth range of the target). This preliminary model can be used as a starting point for optimized inversion. In this paper, the authors show how several types of electromagnetic and gravity attributes can cooperate with seismic information for rapid delineation of the main geological trends in complex exploration areas. Moreover, the authors show how these attributes also can be complementary for identification of hydrocarbon reservoirs and for restricting the range of possible interpretive models. The authors use both synthetic tests and real examples for supporting their integrated method, which is based on combined seismic, electromagnetic, and gravity information.

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.

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 A new joint inversion algorithm to directly estimate reservoir parameters by combined seismic amplitude versus offset (AVO) and marine controlled source electromagnetic (CSEM) data is presented. The approach relies on a rock-properties model to link the geophysical parameters to the reservoir parameters. Tests of the concept on one-dimensional models demonstrate improved fluid saturation and porosity estimates for joint AVO & CSEM data inversion compared to AVO alone. Errors in the rockproperties model parameters introduce errors of comparable size in the joint inversion reservoir parameter estimates.

Numerous publications have dealt with estimations of resistivity from elastic parameters and vice versa. Attempts have been made in the cross-property relationship of elastic and electric properties, in particular, velocity to resistivity using different parameters, such as porosity and water saturation. These types of transforms are currently used to predict background seismic velocities and resistivities, or even start models for seismic or controlled source electromagnetic (CSEM) inversions. However, they are not reliable predictors because they depict the regional elastic or electric variations with limited accuracy. We present a novel approach for the development of models capable of estimating the regional subsurface resistivity based on information from regional wells and seismic inversions. We apply multivariate nonlinear regression on data derived from regional wells and seismic inversions and subsequently produced an estimation of subsurface horizontal resistivity that could be either used as a direct hydrocarbon indicator or provide a constraint on the horizontal resistivity in anisotropic CSEM inversions. We have verified the validity of the approach using two data sets from the Norwegian continental shelf. We found very good agreement between the borehole-measured and predicted resistivity.

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.

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.

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.

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

Improved reservoir reservoir management and production optimisation demands require accurate characterisation of reservoir properties and their changes through time. Advances in geophysical data acquisition and interpretation have led to significant improvements in the remote imaging of earth structure and properties. However, when only a single data type is considered, ambiguities in the interpretation can remain. Here we will concentrate on three contrasting methods: surface seismic, marine controlled source electromagnetic (CSEM) and well-log data. It is clear that a careful combination of all three data types can supply information that is not available, or is unreliable from any one data type alone. By integrating complementary sources of information and exploiting the strengths of each, estimates of rock and fluid properties such as gas saturation and porosity can be obtained with greater confidence than from any one data type alone. We illustrate this using an example based on the Luva discovery in the Norwegian sea. Introduction There are a range of physical parameters we can use to investigate the earth and properties of fluids within it. Well logs provide a high resolution measurement of the properties of a reservoir and the surrounding strata, however properties can only be determined in a small area local to the well. Often a measurement of reservoir properties across the extent of a field are desirable for reservoir management or production optimization and for this we must turn to remote sensing methods. In the well logging industry both electric and acoustic properties are routinely collected and analysed to study lithology, reservoir properties and the properties of fluids within them: it would be almost unthinkable to collect one data type without collecting the other, since this would result in an incomplete dataset that left gaps in our understanding of the reservoir and surrounding strata. Until recently the same logic has not been applied in geophysical surveying and data analysis. Seismic methods, sensitive to acoustic properties only, have been the primary tools used in exploration, reservoir appraisal and monitoring. These methods are extremely powerful. Seismic data are commonly used to provide images of the sub-surface, and develop high resolution geological models of structure and stratigraphy. Amplitude variation with offset (AVO) and inversion for acoustic and elastic impedance may also be used to constrain properties such as elastic moduli and density. However seismic data alone in many situations cannot give a complete picture of the reservoir.

A scaled experiment for the verification of the SeaBed Logging 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.