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Controlled-source electromagnetic (CSEM) data are sensitive to the subsurface resistivity distribution, but 3D inversion results are ambiguous, and in-depth interpretation is challenging. Resolution and sensitivity analysis as well as the influence of noise on resolution have been used to quantify 3D inversion performance. Based on these numerical studies, a land-based CSEM survey was designed and carried out at the Schoonebeek oil field, the Netherlands. The acquired data were processed and subsequently inverted for the resistivity distribution. The 1D and 3D inversion of horizontal electric-field data show the reservoir at the right depth, matching well-log data without using a priori knowledge about the actual reservoir depth. We used a 1D model with fine layering as a starting model for 3D inversion. Synthetic data inversions and sensitivity tests demonstrate that resistive or conductive bodies inside the reservoir zone may be well-detectable with our limited acquisition geometry. Spatial variations in the reservoir resistivity are visible in the measured data and after inversion by assuming good knowledge of the background resistivity distribution. The reservoir resistivity and size, however, have to be interpreted with care considering the intrinsically low resolution of electromagnetic (EM) which is further reduced by manmade EM noise. INTRODUCTION Since the first survey of an oil field offshore Angola using marine controlled-source electromagnetic (CSEM) sounding for determining the absence or presence of hydrocarbons in a known reservoir (Ellingsrud et al., 2002), the CSEM method has gained a lot of interest in the industry. The marine CSEM method is generally recognized to give additional information and is especially applied for hydrocarbon exploration, primarily with the objective to derisk drilling activities (Darnet et al., 2007; Constable, 2010; Fanavoll et al., 2014). Although in marine CSEM surveys, dense 2D profiles or complex 3D grids with tens of transmitter tow lines and hundreds to thousands of receiver deployments are feasible (Constable, 2010), such source and receiver coverage is very difficult to achieve for terrestrial surveys because of various logistical constraints (access restrictions, urbanization, presence of infrastructure, and noise sources) and typically limited equipment availability. In addition, deploying sources of sufficient strengths is difficult. In contrast to the marine case in which the transmitter is situated in the most conductive area, the seawater, land transmitters are usually situated on the earth’s surface, and the source electrodes are deployed in a medium much more resistive than sea water, which limits achievable source current amplitudes. Furthermore, more near-surface heterogeneities exist on land than at the seafloor and near-electrode heterogeneities can result in more complex electric-field patterns. Although electromagnetic (EM) methods were initially developed on land (Streich, 2016), the mentioned problems and challenges of land EM make it difficult to replicate the marine EM exploration success on land. To date, the seismic method is the principal geophysical method that is routinely applied for hydrocarbon exploration on land. Seismic wave-propagation method provides higher resolution than the diffusive CSEMmethod. However, CSEMmethods have the advantage to be sensitive to resistive objects and to spatial variations in the resistivity. They provide generally higher resolution than potential-field methods such as gravity (Li and Oldenburg, 1998; Dell’Aversana et al., 2012). Large-scale EM surveys are not commonly carried out, but many wells exist with resistivity logs. Gathering additional EM data would provide knowlManuscript received by the Editor 10 January 2017; revised manuscript received 23 August 2017; published ahead of production 13 November 2017; published online 28 December 2017. Delft University of Technology, Department of Geoscience and Engineering, Delft, The Netherlands. E-mail: A.M.Schaller@tudelft.nl; G.G.Drijkoningen@ tudelft.nl. Shell Global Solutions International BV, Amsterdam, The Netherlands. E-mail: rita.streich@shell.com. GFZ German Research Centre for Geosciences, Potsdam, Germany. E-mail: oliver.ritter@gfz-potsdam.de. © 2018 Society of Exploration Geophysicists. All rights reserved. WB1 GEOPHYSICS, VOL. 83, NO. 2 (MARCH-APRIL 2018); P. WB1–WB17, 26 FIGS. 10.1190/GEO2017-0022.1 D ow nl oa de d 01 /2 5/ 18 to 1 31 .1 80 .1 30 .2 42 . R ed is tr ib ut io n su bj ec t t o SE G li ce ns e or c op yr ig ht ; s ee T er m s of U se a t h ttp :// lib ra ry .s eg .o rg / edge of the resistivity distribution inside an oil or gas field, which is essential to evaluate its processes during hydrocarbon production or steam injection. Numerical feasibility studies on land exist (Wirianto et al., 2010) and show that subsurface responses are weaker compared with marine CSEM responses and are more complicated to interpret due to near-surface inhomogeneities. Up to now, only a few academic EM surveys and a small number of industry surveys were conducted, which can be considered technology trials rather than commercial surveys (Streich and Becken, 2011; Grayver et al., 2014; Tietze et al., 2015; Streich, 2016). Grayver et al. (2014) showed that for a land CSEM survey, it is possible to get good 3D inversion results consistent with regional geology. They deployed 39 5C receivers along an 11 km long line centered at a CO2 injection site and eight CSEM transmitters well distributed around the area. Several 3D inversion algorithms have been developed over the last decade to reconstruct resistivity models from marine and land EM data (Commer and Newman, 2008; Plessix and Mulder, 2008; Grayver et al., 2013; Oldenburg et al., 2013; Schwarzbach and Haber, 2013; Zhdanov et al., 2014). A recent review of the latest developments can be found in Newman (2014). In this work, we study the applicability of land CSEM for recovering the resistivity distribution inside a hydrocarbon reservoir by 1D and 3D inversions. The underlying mathematical theory of EM inversion is well-established, but the practical application to real data requires thorough analysis of uncertainties in the inversion result. Therefore, we numerically investigate resolution capability in terms of the survey geometry, noise distortion, reservoir depth, and resistivity distribution inside the reservoir. We use a deterministic inversion approach, in which gradients derived from the forwardmodel process are used to update a sequence of models. Receivers are either inverted separately or together, and their inversion results are compared. Based on the results of the analysis, a target-oriented land CSEM acquisition field setup was designed for the Schoonebeek region in the Netherlands where steam is injected into an oilbearing reservoir at a depth of approximately 700 m for enhanced oil recovery. The obtained data were inverted for the resistivity distribution inside the reservoir where the injected steam may lead to more complex resistivity patterns close to the injector and producer wells. We show how a sparse source-receiver configuration has the potential of resolving complex resistivity patterns inside the reservoir zone. Furthermore, we discuss to what extent small-scale variations of resistivity can be detected. INVERSION ALGORITHMS 1D inversion We try to find a model for which we can compute data that will best fit the measured data. Finding this best-fitting model requires iterative (forward) modeling in which after every iteration, the model can be updated. This was done using a reflectivity forward modeling code (Streich and Becken, 2011; Streich et al., 2011). To minimize the number of iterations necessary to find the best-fitting model, we use a Gauss-Newton-type method to compute model updates based on an objective function that we seek to minimize. The objective function is given by f1D 1⁄4 kWdðd − FðmÞÞk2 þ μkWmð∂zðm −m0ÞÞk; (1) where the first term is the misfit of the model’s computed response FðmÞ to the data d that are the real and imaginary parts of the measured electric-field data. The data vector d can contain data from multiple transmitters, receivers, and frequencies. The data weights are defined as Wd 1⁄4 diagðwiÞ, where the weights wi are calculated by multiplying error estimates with the absolute value of the data. They weigh the relative contribution of each datum to the misfit. Data with large errors are down weighted to limit their influence, whereas data with small errors will have a larger impact on the total misfit. The second term is a norm of the model roughness and is computed by applying a differencing operator ∂z, a matrix of first derivatives with respect to depth, to the elements of the model vector m, andm0 is a reference model, e.g., the starting model. We use a bounded logarithmic transform of the conductivity σh;v to the model parameters (Grayver et al., 2013), where σh;v represents horizontal and vertical conductivities. For the logarithmic transform, upper and lower conductivity bounds of 10−4 S∕m (10;000 Ωm) and 1.5 S∕m (2∕3 Ωm) were used. The variable Wm is a diagonal weighting matrix that weighs the model variations and can consist of different measures of the model norm (Farquharson and Oldenburg, 1998); μ is a regularization parameter that weighs the dataand model-dependent terms of the objective function and contains a reduction exponent that gradually decreases the regularization (“cooling”) at each iteration (Haber et al., 2000). The regularization parameter at iteration n is computed as μ 1⁄4 ðμiÞ∕ðnþ 1Þp where the regularization parameter μi weighs the dataand model-dependent terms and p is a parameter that defines by how much the influence of regularization is reduced at each iteration. For the modeling study, a simple least-squares weighting of the model variations μi 1⁄4 0.05 and p 1⁄4 1.67 was used. By minimizing the first-order d

A land-based controlled-source electromagnetic method for oil field exploration: An example from the Schoonebeek oil field