Exploration Geophysics - Volume 52, Issue 1, 2021

Bulk modulus which relates seismic attributes to rock and fluid properties is considered as an important parameter for seismic reservoir characterisation workflows, and can provide useful information for such studies. However, its measurement and modelling approaches are yet to be sufficiently addressed and discussed especially for carbonate rocks. In this research, in order to provide more information about bulk modulus, nine samples from a carbonate oilfield were subjected to static and dynamic investigations under dry and saturated conditions. Then, the dynamic modulus was calculated using well log data and ultrasonic measurements. In order to obtain static data, multi-stage triaxial compression tests were performed on the vertical plugs extracted from conventional cores taken at different selected depths. The results showed that static and dynamic bulk moduli follow similar trends, although the value of dynamic modulus is usually higher. Furthermore, several empirical relationships based on simple linear regression and forward and backward stepwise multiple linear regressions were developed to relate measured static and dynamic bulk moduli for the given field, then proposed equations were evaluated by ANOVA analysis. Besides, dry and saturated bulk moduli were modelled using Xu and Payne rock physics model. Here, saturated bulk modulus was modelled using either dynamic or static dry bulk moduli within the Gassmann’s theory. It is observed that Gassmann’s equation gives a more accurate result using dynamic data rather than static ones. The poor Gassmann’s equation prediction under static condition could be attributed to microcracks in the samples and the uncertainty associated with this theory for complex pore geometry. The outcomes of this study can, furthermore, provide the necessary information and relationships for rock physics (e.g. shear wave estimating and fluid substitution modelling) and geomechanical (e.g. CO2 injection and compaction forecast) studies for exploration and development programs.

Microseismic monitoring provides important information on the locations and moment tensors of microseismic events, and this information can be used to understand the behaviour of the fractures more completely. Characterisations of fractures are used to investigate flow paths and for noninvasive investigation of shale gas sites, geothermal developments, and radioactive waste storage/disposal sites. The locations of microseismic events can be used to estimate the geometry and distribution of fractures and faults, and the moment tensors provide information on the orientations of fracture planes and event magnitudes. We propose an effective fracture network imaging method using microseismic event locations and their moment tensors. For this purpose, the conventional Random Sample Consensus (RANSAC) method was improved so that fracture planes could be extracted quickly and efficiently from point cloud data. In addition, through the application of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to the microseismic event location data and moment tensor information, the accuracies of the locations and the orientations of fracture planes were improved. The developed algorithm was applied to synthetic data, including event locations and noise errors. The results showed that the developed method could extract the fracture planes reliably. Furthermore, the input parameters used in the proposed algorithm were examined, and the results of the conventional RANSAC method and the developed algorithm were compared. Finally, our method was applied to microseismic field data obtained from a shale gas/oil play, and the result accurately depicted the dominant strike of fracture planes.

The modelling technique contributes to understanding noise nature and properties. Prior work has established a primary random noise model in the homogeneous medium, however, this strict assumption of the medium may be not valid under the actual environment so that will reduce the modelling accuracy. Therefore, in this paper, a random noise model is established in the mixed heterogeneous medium to improve the modelling accuracy, so the scattered mechanism is used to describe the random noise field in the heterogeneous medium. Since the perturbation method is always applied to solve the scattering problem, the noise wave field can be regarded as the superposition of the perturbation wave field and the unperturbation wave field. Consequently, the random noise model reveals that the desert random noise is mainly caused by the wind, and it concentrates on 1–20 Hz. A detailed comparison is made between the noise model established in the homogeneous medium and the proposed noise model, the results illustrate that the proposed noise model is more similar to the actual noise. Besides, for embodying the model’s practical application value, the proposed noise model is adopted as the background noise to determine the VMD (variational mode decomposition) denoising parameters. The satisfactory denoising performance supports the usefulness of the proposed noise model for the random noise attenuation.

Advanced seismic detection technology utilising random roadway sources is advantageous method adapting to the development of dynamic intelligent technologies for the detection of hidden and disastrous geological structures. However, roadheaders generate complex signals, that are continuous and random with seismic wavelets that are wider and longer than conventional source wavelets, and these complex wavelets cannot be directly subjected to data processing. To address this problem, based on two basic techniques, namely, deconvolution and cross-correlation, a cross-correlation algorithm in the deconvolution domain is proposed to process continuous random roadheader signals, convert those signals into normal-impulse seismic data, and extract the arrivals of direct and reflected waves and other information. First, the feasibility of the algorithm is explained based on a derivation of theoretical formulas. Then, to verify the applicability of the algorithm to the processing of actual data, a random signal from a tamping source is processed; the continuous random signals are successfully converted into impulse signals, and the extracted in-phase direct waves are clear with good continuity. Moreover, the continuous random signal processing results are compared with an active source signal, and good consistency is found, verifying the effectiveness of the algorithm. Finally, an application analysis of the algorithm is carried out using an actual acquired roadheader source signal. After implementing the algorithm, the random roadheader signal is successfully converted into a normal-impulse seismic signal, and the direct wave arrivals are extracted. Based on the arrival times of the direct waves, the longitudinal wave velocity of the coal seam is calculated as 1918 m/s, which is consistent with the actual velocity. A comprehensive analysis of the test results confirms that the cross-correlation algorithm in the deconvolution domain is both feasible and effective and that the processed seismic signals can be used for subsequent processing and interpretation.

Chirp sub-bottom profilers (SBP) provide centi-to-decimetre resolution, seismic data with applications for various geophysical and geological purposes. To verify the field application of imaging of a buried target with a cost-effective and easy-to-apply pseudo-3D Chirp SBP survey, we explored the buried site of an ancient wooden shipwreck off the west coast of Korea before underwater excavations. The survey was conducted using a commercial 2D Chirp SBP system with a newly devised recording system that preserved the true polarity of the chirp signal. To produce high-resolution 3D Chirp SBP data from 2D Chirp SBP datasets recorded by the novel system, an optimal data processing sequence, consisting of a first phase of 2D data processing and a second phase of 3D data processing was designed. The first, 2D phase, included the estimation of a source sweep signature, cross-correlation, and deconvolution using an inverse filter. The resulting resolution of the 2D Chirp SBP data was better than that of the enveloped data provided by the commercial acquisition system. The second phase of 3D data processing included gathering 3D datasets, redistributing of ping positions, and static correction. To improve the consistency of the seismic events and reduce the repetitive corrections (swell, tidal, tie, and residual corrections), a static correction was based on multi-beam echo sounder data. The amplitude variation near the shipwreck was clearly apparent in the time slice from the final pseudo-3D Chirp SBP dataset with a bin size of 2.0 m (crossline) × 0.6 m (inline). Through 3D rendering, the buried ancient shipwreck with dimensions of 5 m (width) × 12 m (length) × 2 m (depth) was imaged successfully.