Scott C. Hornbostel

Field tests of electroseismic hydrocarbon detection

Geophysicists, looking for new exploration tools, have studied the coupling between seismic and electromagnetic waves in the near-surface since the 1930s. Our research explores the possibility that electromagnetic-to-seismic (ES) conversion is useful at greater depths. Field tests of ES conversion over gas sands and carbonate oil reservoirs succeeded in delineating known hydrocarbon accumulations from depths up to 1500 m . This is the first observation of electromagnetic-to-seismic coupling from surface electrodes and geophones. Electrodes at the earth’s surface generate electric fields at the target and digital accelerometers detect the returning seismic wave. Conversion at depth is confirmed with hydrophones placed in wells. The gas sands yielded a linear ES response, as expected for electrokinetic energy conversion, and in qualitative agreement with numerical simulations. The carbonate oil reservoirs generate nonlinear conversions; a qualitatively new observation and a new probe of rock properties. The...

Applications of seismic polarization analysis

Multicomponent seismic recording contains a wealth of information. A major challenge is to distill this information into something manageable. Polarization analysis is a technique for simplifying the situation by extracting two simple measures: ellipticity and directionality. These measures can be obtained quickly from the data covariance matrices and are sufficient for data analysis or for the design of weighting functions for seismic wave‐type selection. Data analysis using these measures may include the study of ellipticity to discern anisotropy and the use of ellipticity and directionality for a quick survey of the various wave types in the data. Applications of the related weighting functions include the removal of ground roll, the separation of converted waves or refractions, and the removal of out‐of‐plane arrivals.

Spatial prediction filtering in the t-x and f-x domains

The predictability of seismic signals from nearby traces can be a powerful tool for reducing random or locally coherent noise. The choice of algorithm to reduce noise for a given application is a function of the data signal and noise characteristics. When the signal and noise are relatively consistent over a given design window, an f-x domain Wiener‐filter approach can be used. For cases in which the data are time‐ or space‐varying, a new approach using 2-D adaptive filtering in the t-x domain can be very effective. In either of these approaches, a prediction trace‐gap can often be successfully used to remove locally coherent noise when lateral signal changes are not too rapid.

Electromagnetic-to-seismic conversion: A new direct hydrocarbon indicator

We present novel methods that directly detect hydrocarbons based on conversions between electromagnetic and seismic energy. We first introduce the subject and then discuss the experimental methods in three field tests. Next, we present the results of the tests and then end with a few model results and thoughts about future applications.

Field Tests of Electroseismic Hydrocarbon Detection

Geophysicists, looking for new exploration tools, have studied the coupling between seismic and electromagnetic waves in the near-surface since the 1930s. Our research explores the possibility that electromagnetic-to-seismic ES conversion is useful at greater depths. Field tests of ES conversion over gas sands and carbonate oil reservoirs succeeded in delineating known hydrocarbon accumulations from depths up to 1500 m. This is the first observation of electromagnetic-to-seismic coupling from surface electrodes and geophones. Electrodes at the earth’s surface generate electric fields at the target and digital accelerometers detect the returning seismic wave. Conversion at depth is confirmed with hydrophones placed in wells. The gas sands yielded a linear ES response, as expected for electrokinetic energy conversion, and in qualitative agreement with numerical simulations. The carbonate oil reservoirs generate nonlinear conversions; a qualitatively new observation and a new probe of rock properties. The hard-rock results suggest applications in lithologies where seismic hydrocarbon indicators are weak. With greater effort, deeper penetration should be possible.

Waveform design for electroseismic exploration

Electroseismic (ES) exploration remotely identifies the presence of hydrocarbons using the conversion of electromagnetic energy to seismic energy. These conversions are relatively larger in a porous, permeable resistive body (such as an oil or gas reservoir) when compared with background conversions. Typical ES signals, however, may be several orders of magnitude below the expected ambient noise levels, thus requiring repetitions of long source sequences. In addition, the ES signals may vary linearly or nonlinearly with the input current. Two classes of coded waveforms are presented for the detection of these linear and nonlinear conversions. The linear conversions are detected using a Golay sequence pair with side lobes that cancel when the correlated pair is summed. A set of pseudo‐random binary sequences (PRBSs) can be modified to detect a nonlinear squared response with minimal side lobes while removing undesired linear conversions. These source waveforms are implemented in the power waveform synthesizer (PWS) that is capable of switching three‐phase power to create the specified waveforms. The PWS is an important part of the overall electroseismic exploration system that includes source electrodes, pickup‐free receivers, and acquisition and processing features aimed at effective detection of small ES signals.

Seismic system analysis; a case study from the Gulf of Mexico

It is often difficult to predict the effects of change in seismic acquisition or processing on data quality. To address this difficulty, ensemble averaging can be used to estimate the final stacked-trace effects of altering aspects of the seismic acquisition or processing. This method of studying the seismic system begins by modeling of an ensemble of data gathers based on some geological region of interest. The model gathers are subsequently processed to create stacked traces that are compared with related reference traces to make signal-to-noise ratio (SNR) or bandwidth measurements. The ensemble average of these stacked-trace measurements can then be examined while some aspect of the simulated acquisition or processing is adjusted. The result of this analysis is a plot illustrating the sensitivity of the seismic system (i.e., the stacked-trace quality) to the selected parameter under investigation. This ensemble averaging approach was used to study system sensitivities for a data set collected in the Gulf of Mexico. The specific issues examined in this analysis include source and receiver parameters, ambient noise levels, spatial sampling, velocity picking, velocity errors, and stretch muting. Of the parameters studied, the final system output was most sensitive to velocity errors. Even differences of 1-2% in the stacking velocity led to noticeable degradation of the stacked-trace bandwidth and SNR. This velocity sensitivity was evident in both the model and field data. Certain parameters, such as gun volumes, were less important for typical values. The ambient noise level and spatial sampling effects were similarly less important except in the deeper portions of the data (where the unstacked SNR was fairly low). These insensitivities are interesting because they imply potential cost savings. The percent stretch-mute study was interesting because SNR and bandwidth were optimized with different mutes. All study results by design, are, tied to a specific data area. Nonetheless, these findings may provide an initial direction for system studies in other areas.

A noise-optimized objective method for predictive deconvolution

Predictive deconvolution filters are designed to remove as much predictable energy as possible from the input data. It is generally understood that temporally correlated geology can cause problems for these filters. It is perhaps less well appreciated that uncorrelated random noise can also severely affect filter performance. The root of these problems is in the objective function being minimized; in addition to minimizing predictable multiple energy, the filter is attempting to simultaneously minimize the temporally correlated geology and the random‐noise energy. Instead of minimizing the input trace energy, an alternative objective function for minimization can be defined that is the result of a linear operator acting on the input data. Ideally this alternative objective function contains only the targeted noise (e.g., multiples). The linear operator that creates this objective function is designated as the “noise‐optimized objective” (NOO) operator. The filter that minimizes this new objective function...

Multiple suppression with time‐varying gaps and maximized polarity reversals

Another factor that can degrade predictive deconvolution filters is the presence of random noise. At a signal-to-random noise ratio of one, for example, half the multiple remains after filtering. The root of this random-noise problem is in the objective function that is being minimized (i.e., the trace energy). In this paper, a “maximum rate-of-polarity-reversal” (MAXRPR) objective is considered. This function is found to be less sensitive to random noise.

Noise-optimized objective filtering and the geologic correlation problem

Predictive deconvolution filters are designed to remove as much correlated energy as possible from the input data. It is generally understood that this creates a problem when the primary geology is correlated. The root of this problem is in the objective function that is being minimized in addition to minimizing the correlated noise energy, the filter is attempting to simultaneously minimize the primary signal energy.