Seismoelectric Data Research Articles

Water Table and Permeability Estimation From Multi‐Channel Seismoelectric Spectral Ratios

AbstractRecent developments in predicting and interpreting seismoelectric (SE) signals suggest a great potential for studying near‐surface hydrogeological properties, particularly in the vadose zone. Previous studies have revealed that the SE spectral ratios obtained from earthquake‐triggered SE data contain valuable hydrogeological information concerning porous media (e.g., permeability, porosity, fluid viscosity, and salinity). This study introduces Multi‐Channel SeismoElectric Spectral Ratios (MC‐SESRs) by considering an active seismic source acting on the ground surface. The frequency‐ and saturation‐dependent excess charge density is adopted to calculate the cross‐coupling coefficients. Applying a supervised learning task based on a flat neural network, the so‐called “broad learning (BL)” model, to map and extract the features of MC‐SESRs data, we seek to determine the permeability and the water table depth. Our results indicate that (a) MC‐SESRs are sensitive to the water table depth and permeability; (b) using more traces of SESRs data for inversion can increase accuracy; and (c) the changing water table can be rapidly determined by the MC‐SESRs by resorting to the BL inverse model, and it can attain an excellent accuracy while disturbed by data noise and misspecified model parameters (e.g., porosity and permeability) with errors of up to 20%. The proposed MC‐SESRs inversion has potential applications for non‐invasive monitoring in shallow porous media (e.g., frost thawing and geothermal upwelling).

Bayesian inversion of joint SH seismic and seismoelectric data to infer glacier system properties

ABSTRACTIn glacial studies, properties such as glacier thickness and the basement permeability and porosity are key to understand the hydrological and mechanical behaviour of the system. The seismoelectric method could potentially be used to determine key properties of glacial environments. Here we analytically model the generation of seismic and seismoelectric signals by means of a shear horizontal seismic wave source on top of a glacier overlying a porous basement. Considering a one‐dimensional setting, we compute the seismic waves and the electrokinetically induced electric field. We then analyse the sensitivity of the seismic and electromagnetic data to relevant model parameters, namely depth of the glacier bottom, porosity, permeability, shear modulus and saturating water salinity of the glacier basement. Moreover, we study the possibility of inferring these key parameters from a set of very low noise synthetic data, adopting a Bayesian framework to pay particular attention to the uncertainty of the model parameters mentioned above. We tackle the resolution of the probabilistic inverse problem with two strategies: (1) we compute the marginal posterior distributions of each model parameter solving multidimensional integrals numerically and (2) we use a Markov chain Monte Carlo algorithm to retrieve a collection of model parameters that follows the posterior probability density function of the model parameters, given the synthetic data set. Both methodologies are able to obtain the marginal distributions of the parameters and estimate their mean and standard deviation. The Markov chain Monte Carlo algorithm performs better in terms of numerical stability and number of iterations needed to characterize the distributions. The inversion of seismic data alone is not able to constrain the values of porosity and permeability further than the prior distribution. In turn, the inversion of the electric data alone, and the joint inversion of seismic and electric data are useful to constrain these parameters as well as other glacial system properties. Furthermore, the joint inversion reduces the uncertainty of the model parameters estimates and provides more accurate results.

Numerical simulation of seismoelectric effect for monitoring foam propagation through a reservoir

The seismoelectric phenomenon describes generation of electromagnetic fields when seismic wave passes through fluid saturated porous media. Discovered in the 1930s, the seismoelectric effect have gained attention only in the last few decades as a tool of great potential in geophysical applications. Despite some usage in mineral exploration and ground water flow, routine field applications of this phenomenon are still under development. Nevertheless, recent advancements show great potential of the seismoelectric method in the oil and gas industry. It is now known that several rock and fluid properties affect the seismoelectric response, hence application of this response may be used for reservoir characterization, subsurface imaging and mapping, and monitoring fluid propagation through a reservoir. In this paper, we numerically studied the prospect of monitoring foam propagation through a reservoir using the seismoelectric phenomenon. The reservoir model used in the study was developed using real field data. The virtual pilot area comprised of a cross-well domain between injector well and producer well, where pre-generated foam was continuously injected into the reservoir for the period of 150 days. The seismoelectric data acquisition was modeled in the domain, where the seismic source was deployed in the injection well, and the receiving stations were installed in production well accordingly. The results of the seismoelectric surveys, with interval of 30 days, produced both seismic and seismoelectric signals received by the stations. The processed data, coupled with the average seismic velocity and arrival times of the seismoelectric conversions, were analyzed with regard to determining the locations of the displacing foam front. Subsequent comparison and analysis showed high accuracy match and consistency between the true front positions obtained from the reservoir simulator and the determined locations produced using the seismoelectric method. Additionally, to demonstrate how the seismoelectric signals can be processed in field trials, we applied noise to the same synthetic data used to estimate the foam front positions. We observed that the occurrence of the seismoelectric signal was imperceptible due to the prevailing noise. Subsequently, we applied a filtering method to reduce the effect of the noise that showed great results in uncovering the presence of the seismoelectric conversion in the electrogram while preserving amplitudes and time arrivals of the seismoelectric, seismic and co-seismic signals. According to the obtained findings, we can state that later computations of locating the foam front would yield results with the same accuracy as the noise-free synthetic signals.

Seismoelectric acquisition in an arid environment

Most seismoelectric surveys to date have been acquired on a small scale in temperate regions. Our objective was to establish if seismoelectric data could be acquired on a large scale in an arid environment. In April 2011, we acquired over 21,000 traces of 2D seismoelectric data at an arid site in Abu Dhabi, United Arab Emirates. The test also included seismic measurements made using WesternGeco?s UniQ single-sensor acquisition system. The source used for the acquisition was an 80,000 lb tracked Desert Explorer vibrator, the largest hydraulic Vibroseis source ever used for seismoelectric acquisition. This large source was used to attempt to overcome the low signal to noise issues inherent in seismoelectric acquisition that have been exacerbated in the past by the use of low energy sources. We successfully acquired high quality data with coseismic signal present to the limits of our acquisition (420 m offset and 2 s record length). Our current equipment is, however, ill-suited to rapid deployment, having far too many components. The acquisition of large seismoelectric datasets, such as that described here, enables the data to be viewed in the common receiver domain enhancing data processing and bad trace identification.

Curvelet-based seismoelectric data processing

Seismoelectromagnetic conversions are induced through electrokinetic phenomena occurring when passing seismic waves induce relative fluid to solid displacements. Information on porous material properties provided by the type I coseismic electric field accompanying surface and body waves is limited to the vicinity of the electrical receivers, as opposed to the type II electromagnetic interface response (IR) which can help detect porous contrasts and/or fluid changes within the subsurface ‘at depth’. For general field geometries, the problem is that type II disturbances exhibit lower amplitudes than type I fields and are difficult to access directly. Hence, separating both types of waves is a critical step when processing seismoelectric data. Synthetic seismograms and electrograms, generated with a full-waveform seismoelectric forward modelling code written by Garambois & Dietrich, enabled us to study the behaviour of the IR's characteristic dipolar amplitude pattern recovered through filtering. To better preserve the IR amplitudes, we have developed a new filtering strategy based on the Fast Discrete Curvelet Transform. Seismic or seismoelectric wave fronts can be optimally described using this multiscale decomposition over multidirectional anisotropic needle-shape structures. We have built a mask in the curvelet domain zeroing out seismoelectric samples corresponding to non-zero samples from the accelerogram, thus taking advantage of the relationship between seismic and seismoelectric waves for type I fields. This mask consists of a threshold function combined with a Gaussian distribution promoting horizontal (i.e. zero-slowness) directions. When applied to synthetic data, this filter enabled to successfully extract the IR although less altering its dipolar radiation pattern than the conventional dip-based techniques. This strategy was successfully applied to a seismoelectric data set acquired in sedimentary deposits, and permitted to isolate an IR generated at the water table.

Imaging with cross-hole seismoelectric tomography

We propose a cross-hole imaging approach based on seismoelectric conversions (SC) associated with the transmission of seismic waves from seismic sources located in a borehole to receivers (electrodes) located in a second borehole. The seismoelectric (seismic-to-electric) problem is solved using Biot theory coupled with a generalized Ohm's law with an electrokinetic streaming current contribution. The components of the displacement of the solid phase, the fluid pressure, and the electrical potential are solved using a finite element approach with Perfect Match Layer (PML) boundary conditions for the seismic waves and boundary conditions mimicking an infinite material for th electrostatic problem. We develop an inversion algorithm using the electrical disturbances recorded in the second borehole to localize the position of the heterogeneities responsible for the SC. Because of the ill-posed nature of the inverse problem (inherent to all potential-field problems), regularization is used to constrain the solution at each time in the SC-time window comprised between the time of the seismic shot and the time of the first arrival of the seismic waves in the second borehole. All the inverted volumetric current source densities are aggregated together to produce an image of the position of the heterogeneities between the two boreholes. Two simple synthetic case studies are presented to test this concept. The first case study corresponds to a vertical discontinuity between two homogeneous sub-domains. The second case study corresponds to a poroelastic inclusion (partially saturated by oil) embedded into an homogenous poroelastic formation. In both cases, the position of the heterogeneity is recovered using only the electrical disturbances associated with the SC. That said, a joint inversion of the seismic and seismoelectric data could improve these results.

Seismoelectric imaging of the vadose zone of a sand aquifer

We have acquired a [Formula: see text] seismoelectric section over an unconfined aquifer to demonstrate the effectiveness of interfacial signals at imaging interfaces in shallow sedimentary environments. The seismoelectric data were acquired by using a [Formula: see text] accelerated weight-drop source and a 24-channel seismoelectric recording system composed of grounded dipoles, preamplifiers, and seismographs. In the shot records, interfacial signals were remarkably clear; they arrived simultaneously at offsets as far as [Formula: see text] from the seismic source. The most prominent signal was generated at the water table at a depth of approximately [Formula: see text] and had peak amplitudes on the order of [Formula: see text]. A weaker response was generated at a shallower interface that is interpreted to be a water-retentive layer. The validity of these two laterally continuous events, and of other discontinuous events indicative of vadose-zone heterogeneity, is corroborated by the presence of reflections exhibiting similar characteristics in a ground-penetrating radar profile acquired along the same line.

Polarisation and slowness of seismoelectric signals: a case study

ABSTRACTIn saturated porous media, seismic compressional waves can generate electric and electromagnetic signals of observable amplitudes. These signals can be recorded with dipoles consisting of two electrodes each. As the generation of seismoelectric signals is connected with properties, such as hydraulic permeability, porosity, and fluid salinity, it is possible that the seismoelectric method could be used in hydrogeophysics for determining these parameters. However, the seismoelectric method still remains in the experimental stage. This is mainly due to the difficulties involved in seismoelectric measurements.We present repeatable seismoelectric field data with exceptionally high signal‐to‐noise ratios and prove that the assumed seismoelectric signals are not artefacts but the desired signal converted from seismic compressional waves at subsurface boundaries. Their amplitude distribution, arrival times and slownesses, as well as their polarisations, are checked against theoretical predictions and comparisons with other methods, such as refraction seismics, borehole logging data and downhole seismics and seismoelectrics. Conventional velocity filters in the frequency_wavenumber domain accentuate the seismoelectric signals converted at subsurface boundaries from incident seismic compressional waves, differentiating them from the usually less informative seismoelectric waves travelling inside compressional waves and being confined to these waves.The recording of three‐component seismoelectric data enabled us to perform a polarisation study showing that the polarisations of the seismoelectric signals encountered are mainly found on the radial and the vertical components and thus follow the predictions. The three‐component data suggest the existence of a composite signal from a thin layer which would have remained undetected with one‐component measurements. Repeatable signals were also encountered on the transverse component which we interpret as 3D effects of a layer dipping in the transverse direction.

Seismoelectric data processing for surface surveys of shallow targets

The utility of the seismoelectric method relies on the development of methods to extract the signal of interest from background and source-generated coherent noise that may be several orders-of-magnitude stronger. We compare data processing approaches to develop a sequence of preprocessing and signal/noise separation and to quantify the noise level from which we can extract signal events. Our preferred sequence begins with the removal of power line harmonic noise and the use of frequency filters to minimize random and source-generated noise. Mapping to the linear Radon domain with an inverse process incorporating a sparseness constraint provides good separation of signal from noise, though it is ineffective on noise that shows the same dip as the signal. Similarly, the seismoelectric signal and noise do not separate cleanly in the Fourier domain, so [Formula: see text]-[Formula: see text] filtering can not remove all of the source-generated noise and it also disrupts signal amplitude patterns. We find that prediction-error filters provide the most effective method to separate signal and noise, while also preserving amplitude information, assuming that adequate pattern models can be determined for the signal and noise. These Radon-domain and prediction-error-filter methods successfully separate signal from [Formula: see text] stronger noise in our test data.

Hydrogeological Mapping using the Seismoelectric Method

Seismoelectric methods are based upon physical properties of the earth that produce electrical signals from seismic waves. Electrokinetic sounding (EKS) is one such method that has great potential for hydrogeological studies as the signal arises from the movement of pore fluids under seismic excitation. In principle, the method should be able to directly map changes in hydraulic permeability, rock porosity and fluid-chemistry. A number of researchers have recently tried to exploit the phenomenon in groundwater problems where conventional methods worked poorly. However, publications of successful case histories to support the theory are rare. This may be due to the very weak amplitudes of the electrical signals generated from the seismic wave, which are millivolts to nanovolts in magnitude, and the presence of cultural noise, which is usually much greater in magnitude.We demonstrate electrokinetic responses from formations more than 50 metres deep in two test areas in Western Australia. One is over a saline palaeochannel and the other is over a freshwater aquifer. The seismic waves were generated from a sledgehammer source and acoustic and electrical data were recorded by a seismic acquisition system. Seismic refraction and reflection data provide seismic velocity information for depth conversion and support the seismoelectric data. The data were then compared to borehole logs to find what physical contrasts were detected. Significant hydrogeological boundaries were detected up to 50 m deep in saline groundwater conditions, and to at least 100 m deep in freshwater aquifers.