The 16th International Symposium on Recent Advances in Exploration Geophysics (RAEG 2012)

Full-waveform inversion method is used to estimate subsurface velocity structure. This method directly utilizes observed waveform data that could deform by the change in the properties of subsurface materials. In general, the difference between the observed and the modeled waveforms are minimized to estimate the subsurface properties in terms of seismic velocities. In contrast to the existing FWI applications, our research focuses directly on the properties in the hydrocarbon reservoir to estimate the fluid distribution and alteration. We solve the elastic problem with FWI, updating P-wave velocity distribution. One　parameter full-waveform inversion based on conjugate gradients algorithm for the synthetic model give us reconstruct the model similar to the true, separated-layer model.

In this study, we developed the 2D inversion program that estimate the permeability structure from Self-potential (SP) profile. We applied these inversion programs to synthetic SP profiles. The synthetic SP profiles, used as observed data for the inversion, are evaluated with modeled slopes with 800m length and 40m height including various permeability anomalies. Four models including the permeable anomalies located in the center of the model are used for the estimation of the performance of our inversion. A priori information of the distribution of streaming current co-efficient, electrical conductivity and the flux volume at the discharge and recharge are given for our inversion. The horizontal zone with high permeability and the vertical zone with low permeability can be reconstructed with our inversion properly. However, the horizontal low permeable and the vertical high permeable zone cannot be imaged clearly. The regional groundwater flow pattern around the permeability anomaly has great effect on the SP pattern on the surface. The consideration of flow pattern around the permeability anomaly and effect on the SP profile are necessary for the accurate inversion of SP data.

A 3D full waveform inversion method is presented using controlled-source electromagnetic (CSEM) method. Using data from a synthetic model, we demonstrate that conductive anomalies around subsurface could be estimated. We discussed the resolution of our CSEM inversion method, considering the orientation of the dipoles of transmitter and receivers. The synthetic inversion examples show that horizontal source dipole gives high resolution for horizontal position of anomaly. We also find that vertical transmitter gives high resolution for deeper position of anomaly. When the 3-components transmitter’s model gradients are considered, the inversion result have high resolution for horizontal and vertical positions. These differences of the inversion results are explained considering the orientation of electric flux. From numerical results, we consider that it is efficient to employ different oriented dipoles of transmitter and receivers for accurate inversion.

Seismic interferometry is an evolutionary new technique in seismic data processing field to obtain virtual shot records. We apply seismic interferometry to earthquake dataset acquired by DONET seafloor seismometers deployed in the Nankai Trough area. We use two horizontal components of seismometer to obtain virtual shot record. We expect that the two horizontal components are dominated by S-wave component. Therefore, the obtained virtual shot record should reflect the subseafloor S-wave velocity structure in the shallow part of the plate subduction zone. We then estimated direction of S-wave anisotropy from Alford rotation applied to obtained virtual shot records. Results show that the estimated directions of S-wave anisotropy are corresponding to the direction of principal share stress estimated from other methods, such as the borehole breakout analysis conducted in the IODP borehole sites, which are located near DONET observatories. Our results imply that seismic interferometry can be used as powerful tool to estimate S-wave anisotropy to monitor stress accumulation process which is underway in the plate subduction seismogenic zone.

Market needs for exploration geophysics are analyzed. Market movements from expert market to non-expert market, from upstream of a project to downstream of it, as well as the unchanging importance of technical differentiation are suggested. The trends of exploration geophysics influenced by recent general technical advancements are reviewed. Advance electronics, software and information technology have enabled the acquisition and interpretation of large datasets. The general public is expecting more with exploration geophysics, and is impatient with uncertainty. To effectively improve exploration geophysics, geophysical methodology including a way to optimize the performance and cost is discussed. To efficiently achieve a quality result, tips to improve the quality of datasets and quality of analysis are also discussed. These reviews and studies are summarized into the development goals of geophysical instruments. Finally, some recent instrument developments conducted by colleagues of the author are introduced. It seems exploration geophysics has a large potential of expanding its application and resulting market. Systematic development based on logical methodology can guide the efficient development of new geophysical instruments.

Data processing of magnetotelluric (MT) survey has been applied frequency by frequency based on the Fast Fourier Transform (FFT), since the FFT gives us the response functions (RFs) of the earth in the frequency domain directly. However, applying FFT processing to MT data may not be optimum. It is because the MT data is in general non-stationary since the source of MT is the transient fluctuation of electric current in the ionosphere. As well known, the FFT assumes time series to be a stationary so that we develop the data processing without FFT. We focus on an IIR filter called ‘pole on pedestal’ that extracts the signal at a specific frequency. Combining this IIR filter and the Hilbert transform, the RFs are calculated in time domain. In addition, it is important to remove the time segments contaminated by noise out of the whole recorded time series before calculating the RFs. Several coherences (for example, partial or multiple coherences) have usually been used to remove the segments contaminated by noise. However, it is known that these coherences are insufficient to select the segments. We apply the maximum entropy method (MEM) to the selection of the segments contaminated by large noise. The MEM searches and removes these contaminated segments easily. As a result we developed the time domain processing of MT data using MEM and IIR filter, and applied this processing to the real data acquired at the Nankai trough. Comparing the conventional and novel data processing, our novel data processing gives us more optimum RFs than the conventional processing.

The amount of oil production in the world is decreasing recently and it is of importance to seek the technological development for enhanced oil recovery (EOR) in place in the subsurface. Seismic stimulation is known as one of the methods of EOR. Recently, many laboratory experiments and field tests have been performed such as water, gas, chemical, or thermal injections to attempt the enhancement of oil production. Numerous observations show that seismic stimulation of oil reservoir may alter water and oil production. But the detailed mechanism of seismic stimulation is not fully understood. We attempt to understand the mechanism of the flux change in viscous laminar flow under oscillating boundary condition to simulate seismic EOR. We focus on flux change by the oscillated boundary wall because of partial high pressure gradient and flow velocity difference. In this study, we analyze a single-phase flow in various pore shapes and scales with two dimensional (2D) Lattice Boltzmann method (LBM). The results show pore scales and shapes are largely related with the flux change, because of the reduction of pressure loss and the flow velocity difference between the wall and flow.