GPR Simulations for pipeline oil drainage Marco A. R. Ceia, marco@lenep.uenf.br & Antônio Abel G. Carrasquilla,abel@lenep.uenf.br Results Abstrct The parameters choice was the key step for model building. The area was chosen as a rectangle with 10 m width and 3 m high. DC relative permitivitty and conductivities values were based in Annan (1992), Parkhomenkho (1967) and Porsani (1999) tables. The examples were idealized in a way to reproduce several situations that can be founded in GPR surveying for mapping contamination extension caused by oil drainage of a metallic pipeline. In these examples the metallic pipe was described as a cylindrical perfect conductor (Giannopoulos, 2002). Its cross-section is shown in all the examples. The other media used in these simulations have the electrical properties shown in Table 1. The pipe is supposed to be buried 1 m downward. A small leakage at the pipe allows oil to flow out (Figure 1). To simplify the models, we suppose that the contamination spreads in a conic geometry. Background Situation (No drainage) This paper aims to show Ground Penetrating Radar (GPR) simulated images of oil drainage from leakage in metallic pipelines. That kind of hazard can contaminate subsurface environment, such a way this underground contamination should be mapped prior to any remediation process. GPR can be used to that, but its success will depend on the subsurface layering environment, mainly the electrical properties of the layers. In this work, several layering models were used as input models for GPRMAX2D, a GPR 2-D forward modeling freeware. The simulated images show that depending on the subsurface environment, none or few processing steps will be required to reveal the contamination plume extension. B A Introduction Pipeline detection and investigation of leakage and fluid drainage play a major part in geophysics applied to downstream problems. Oil drainage may have impact in contaminating subsurface environment, and by consequence, affect socially and economically the nearby population. When that hazard occurs, it could be mapped by geophysical techniques. The strong contrast in electrical properties between oil (or gas), metallic pipes and sediments, makes GPR (ground penetrating radar) one of the best techniques to be used for mapping oil drainage and for pipeline detection. In this paper, a 2-D GPR forward modelling algorithm was used to create simulations of a several drainage situations. The resulting images can help geoscientists in planning field surveys and in the interpretation of simple cases. Drainage Situation Effect of geologic environment (1-layer earth) E D C Method Ground penetrating radar (GPR), also known as geo-radar, has become, in recent years, a popular geophysical technique to study shallow subsurface sediments, as it allows for fast acquisition of high-resolution images of the sedimentary architecture. GPR is based on the reflections of electromagnetic waves, transmitted from a point at the surface through the subsurface. These reflections are caused by changes in the electromagnetic properties of subsurface features, which can be associated to changes in lithology or variation in the water content. Good references for this method can be found in Annan (1992) and Davis & Annan (1989). Figure 1 – Schematics of the modelled situations (without geology). Lithology Relative Permittivity Conductivity (S/m) Dry Sand 4 0.001 Sand partially saturated with water 10 0.01 Wet Sand 20 0.1 Oil 2.8 1e-12 Background Removal (A-C) Background Removal (B-E) 2-D forward modelling Table 1 – Electrical properties of the media used in 2-D modelling. F In all the 1-layer earth models (C, D and E), it was possible to map the oil contamination, but in some cases, some sort of processing step should be applied. In model C, due to signal attenuation, the contamination could not be seen in the modelling output. A background removal, i.e., to subtract C from the no drainage situtation (A), as shown in F, or a special type of gain like AGC, should be applied to enhance this very weak signal. Model D response is different, plume signature is easily recognized by the strong reflectors due high contrast in electrical properties between oil and a low attenuation sandstone. Even so, diffraction alterates the plume geometry (shape), such a way that a migration process will be required to provide a more realistic image. The influence of diffraction is minimized in model E. Pratically, no migration processing is required to reveal the real shape of the plume. Due to low contrast in electrical properties in this model, gain or background removal (G), for instance, should be applied to enhance the plume signature. G A 2D forward modelling algorithm called GPRMAX2D (Giannopoulos, 2002) was used to simulate GPR surveys. That algorithm is based on finite difference time domain (FDTD) method, whose approach to the numerical solution of Maxwell’s equations is to discretize both the space and time continua. It allows model building derived from simple geometric shapes as rectangles, circles and triangles, such a way that the combinations of those shapes can reproduce some complex geological models. To reduce computational requirements, some suppositions are assumed by GPRMAX2D algorithm in order to simplify the models, like: ·          All media (layers) are considered to be linear and isotropic. ·          The GPR transmitting antenna is modelled as a line source. ·          The constitutive parameters are, in most cases, assumed not to vary with frequency. We also choose a 600 MHz central frequency for antennas, 0.1 m for station spacing, 0.25 m for antenna offset and a 50 ns time window. Acquisition was supposed to be common-offset using GPRMAX2D scan mode. Free space layer was used only for computational purposes. Antennas were supposed to be laid down on solid environment (sand). Oil lithology described in Table 1, means sand contaminated with oil.