The results of a 13-day seismic monitoring experiment are presented. It consists in 2 permanent piezoelectric sources, one cemented in depth and the other attached to a surface concrete pad and 28 sensors, 14 at the surface and 14 cemented below the weathering zone. To enhance the signal-to-noise ratio, continuous averaging of the individual SP records is performed providing an average daily SP. 4D attributes are measured on these daily averages. The best repeatability is obtained when both sources and sensors are buried with time and amplitude variations of 6 μs and 0.5% respectively. This extremely high precision level is far above what can be expected from the most accurate surface acquisition methods currently available.

We present a numerical method for solving Maxwell’s equations in the case of an arbitrary two‐dimensional resistivity distribution excited by an infinite current line. The electric field is computed directly in the time domain. The computations are carried out in the lower half‐space only because exact boundary conditions are used on the free surface. The algorithm follows the finite‐element approach, which leads (after space discretization) to an equation system with a sparse matrix. Time stepping is done with an implicit time scheme. At each time step, the solution of the equation system is provided by the fast system ICCG(0). The resulting algorithm produces good results even when large resistivity contrasts are involved. We present a test of the algorithm’s performance in the case of a homogeneous earth. With a reasonable grid, the relative error with respect to the analytical solution does not exceed 1 percent, even 2 s after the source is turned off.

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Calibrating the 4D signal at the well with information obtained from production data is essential for it to be used quantitatively. We have developed a model-based inversion method to estimate the changes of elastic parameters in the reservoir due to production at the well. Our scheme is based on the observation that flow behavior is constrained by the dynamic properties of the layer (i.e., permeability), and, therefore, a layered model should be used to parameterize the inversion. The inversion scheme considers traveltime (inside and below the reservoir, but not in the overburden) and impedance effects implied by the change of elastic parameters (inside the reservoir). Therefore, even at zero offset, we can separate changes in density from changes in P-velocity. When using multiple offset data, we can use an exact formulation for the reflectivity if the base logs (density, P-velocity, and S-velocity) are available, otherwise, an approximation to the exact form can be used. Theoretical and practical analyses have shown that P-velocity is the best resolved parameter followed by density and, finally, S-velocity. Compared to classical data-driven inversion, our procedure introduces fewer artifacts and is less sensitive to tuning because the layered model parameterization introduces the missing low and high frequencies (although the seismic bandwidth plays an essential role in the resolution). This 4D inversion at the well is part of a larger scheme that uses the results obtained by this scheme to extend the inversion to the whole data set.