Vertical Seismic Profiling Data Processing Research Articles

A Novel Method for Determining Geophone Orientations From Zero-Offset VSP Data Constrained by Scalar Field

In vertical seismic profile (VSP) acquisition, the orientation of the three-component geophones in a borehole is often not aligned due to random rotation of the wireline receiver array. Knowing the geophone orientation is crucial for rotating the waveforms back to the position where the geophones are assumed to be aligned. This procedure is critical in three-component VSP data processing as it provides the correct wavefields for subsequent VSP data imaging and inversion. For zero-offset or near zero-offset VSP measurements, the direct P-wave propagates nearly vertically, and the horizontal geophones receive only a small fraction of the direct P-wave energy. The first-arrival in the horizontal component data is often of low signal-to-noise ratio (SNR). As a result, it is difficult to apply conventional first-arrival–based methods for obtaining accurate geophone orientations. On the other hand, a seismic event comprising individual waveform would achieve maximum correlation if all corresponding geophones are aligned. The geometric characteristics, that is, the slope and continuity of a seismic event in the vector wavefield are the same with those of the modules of the vector wavefield. The latter, also known as the scalar wavefield, can be used for scanning geophone orientations based on waveform correlation. In this study, we propose to use the scalar field to extract the slope of seismic events. The orientation of the individual geophone was calculated with the constraint of the slope. We use both synthetic and field data to demonstrate the effectiveness and applicability of the proposed method. The results show that the new method can provide wavefields of horizontal component VSP data with much higher accuracy and resolution.

Deep Learning-Based P- and S-Wave Separation for Multicomponent Vertical Seismic Profiling

Vertical seismic profiling (VSP) helps to derive high-resolution images around the instrumented borehole and is a cost-effective technique for CO₂ storage monitoring. In routine VSP data processing, P- and S-wave separation is a crucial step to extract independent single-mode waves for accurate imaging and interpretation. Conventional wave mode separation involves tedious, subjective, and non-reproducible manual interventions, especially when dealing with complex geology. To better automate the process, we propose a data-driven deep learning-based P- and S-wave separation method. Our method adapts a fully convolutional neural network that simultaneously extracts P- and S-potential data from multicomponent VSP measurements. To reduce the enormous computational cost in wave simulation while constructing training datasets with sufficient kinematic and dynamic variations, we introduce virtual wellbores where synthetic VSP data sampling wide variations in seismic kinematics and dynamics are recorded using only a dozen elastic wave simulations on a single velocity model. We qualify the separation results both directly in data space and in image space after reverse time migration (RTM). Generalization tests on various synthetic models and their corresponding RTM images demonstrate that the proposed strategy provides sufficient sampling of the high-dimensional data space and essentially ensures successful applications of the trained neural network to similar yet different geological scenarios.

A field experiment of walkaway distributed acoustic sensing vertical seismic profile in a deep and deviated onshore well in Japan using a fibre optic cable deployed inside coiled tubing

ABSTRACTA two‐dimensional walkaway vertical seismic profiling survey using distributed acoustic sensing was conducted at an onshore site in Japan. The maximum depth and the deviation of the observation well were more than 4,000 m and 81 degrees, respectively. Among the several methods for installing fibre optic cables, we adopted the inside coiled tubing method, in which coiled tubing containing a fibre optic cable is deployed. The signal‐to‐noise ratio of the raw shot gather was low, possibly due to poor coupling between the fibre optic cable and the subsurface formation resulting from the fibre optic cable deployment method and the existence of considerable tubewave noise. Nevertheless, direct P‐wave arrivals, P–P reflections and P–S converted waves exhibited acceptable signal‐to‐noise ratios after careful optimization of gauge length for distributed acoustic sensing optical processing and the application of carefully parameterized tubewave noise suppression. One of the challenges in current distributed acoustic sensing vertical seismic profile data processing is the separation of P‐ and S‐waves using only one‐component measurements. Hence, we applied moveout correction using two‐dimensional ray tracing. This process effectively highlights only reflected P‐waves, which are used in subsequent subsurface imaging. Comparison with synthetic well seismograms and two‐dimensional surface seismic data confirms that the final imaging result has a sufficiently high quality for subsurface monitoring. We acquired distributed acoustic sensing vertical seismic profile data under both flowing conditions and closed conditions, in which the well was shut off and no fluid flow was allowed. The two imaging results are comparable and suggest the possibility of subsurface imaging and time‐lapse monitoring using data acquired under flowing conditions. The results of this study suggest that, by adopting the inside coiled tubing method without drilling a new observation well, more affordable distributed acoustic sensing vertical seismic profile monitoring can be achieved in fields such as CO2 capture and storage and unconventional shale projects, where monitoring costs have to be minimized.

Application of time-lapse full waveform inversion of vertical seismic profile data for the identification of changes introduced by CO2 sequestration

Seismic methods are frequently used for the purpose of monitoring of time-lapse changes introduced by CO2 sequestration. Surface seismic is often considered as the main tool for monitoring. Vertical Seismic Profile (VSP) is occasionally applied as an auxiliary method. Standard VSP data processing workflow does not provide a quantitative estimate of the time-lapse changes in the physical properties. However, full waveform inversion (FWI) may be used for the purpose of quantitative interpretation. Its ability to employ the whole seismic wavefield (including transmitted, reflected and converted waves) for the purpose of building the models of physical properties can be considered one of its main advantages.We show that time-lapse elastic FWI of VSP data is capable of providing quantitative estimates of time-lapse changes in the medium. A feasibility study is carried out on 2D and 3D synthetic datasets created using full-earth models of the CO2CRC Otway CO2 sequestration site. The inversion workflow obtained from the feasibility study is successfully applied to a field single-offset time-lapse VSP dataset. As a result, FWI provides an image of the time-lapse changes introduced by the injection of supercritical CO2.

Vertical Seismic Profile Wavefield Separation Using Median Filtering Constrained by the Linear Radon Transform

Separation of downgoing and upgoing P-waves and S-waves in an offset 2D/3D vertical seismic profile (VSP) record is a basic step in VSP data processing. Radon transform and median filtering methods are commonly used to separate wavefields. However, with the former, it is hard to strike a balance between the resolution and amplitude preservation, while the latter ignores the variation in the slowness (apparent velocity) of events. In order to solve these problems, we propose a method based on a modified linear Radon transform and dynamic time shifting. We derive the slowness spectrum of an input VSP record through the modified Radon transform. Based on the optimal slope values of the events derived from the slowness spectrum, we align the wavefields by dynamic time shifting. Then, the target wavefields are separated using a median filtering constrained by the linear Radon transform. Our wavefield separation process is iterated until the four kinds of wavefields are separated according to a priority ranking based on the order of the energy spectrum from strong to weak. The results were found to be better than those derived from wave separation using the traditional median filter or those derived using the high-resolution Radon transform method.

Borehole seismic data processing and interpretation: New free software

Vertical Seismic Profile (VSP) surveying is a vital tool in subsurface imaging and reservoir characterization. The technique allows geophysicists to infer critical information that cannot be obtained otherwise. MVSP is a new MATLAB tool with a graphical user interface (GUI) for VSP shot modeling, data processing, and interpretation. The software handles VSP data from the loading and preprocessing stages to the final stage of corridor plotting and integration with well and seismic data. Several seismic and signal processing toolboxes are integrated and modified to suit and enrich the processing and display packages. The main motivation behind the development of the software is to provide new geoscientists and students in the geoscience fields with free software that brings together all VSP modules in one easy-to-use package. The software has several modules that allow the user to test, process, compare, visualize, and produce publication-quality results. The software is developed as a stand-alone MATLAB application that requires only MATLAB Compiler Runtime (MCR) to run with full functionality. We present a detailed description of MVSP and use the software to create synthetic VSP data. The data are then processed using different available tools. Next, real data are loaded and fully processed using the software. The data are then integrated with well data for more detailed analysis and interpretation.In order to evaluate the software processing flow accuracy, the same data are processed using commercial software. Comparison of the processing results shows that MVSP is able to process VSP data as efficiently as commercial software packages currently used in industry, and provides similar high-quality processed data.

Application of 3D vertical seismic profile multi‐component data to tight gas sands‡

ABSTRACTDue to the complicated geophysical character of tight gas sands in the Sulige gasfield of China, conventional surface seismic has faced great challenges in reservoir delineation. In order to improve this situation, a large‐scale 3D‐3C vertical seismic profiling (VSP) survey (more than 15 000 shots) was conducted simultaneously with 3D‐3C surface seismic data acquisition in this area in 2005. This paper presents a case study on the delineation of tight gas sands by use of multi‐component 3D VSP technology. Two imaging volumes (PP compressional wave; PSv converted wave) were generated with 3D‐3C VSP data processing. By comparison, the dominant frequencies of the 3D VSP images were 10–15 Hz higher than that of surface seismic images. Delineation of the tight gas sands is achieved by using the multi‐component information in the VSP data leading to reduce uncertainties in data interpretation. We performed a routine data interpretation on these images and developed a new attribute titled ‘Centroid Frequency Ratio of PSv and PP Waves’ for indication of the tight gas sands. The results demonstrated that the new attribute was sensitive to this type of reservoir. By combining geologic, drilling and log data, a comprehensive evaluation based on the 3D VSP data was conducted and a new well location for drilling was proposed. The major results in this paper tell us that successful application of 3D‐3C VSP technologies are only accomplished through a synthesis of many disciplines. We need detailed analysis to evaluate each step in planning, acquisition, processing and interpretation to achieve our objectives. High resolution, successful processing of multi‐component information, combination of PP and PSv volumes to extract useful attributes, receiver depth information and offset/ azimuth‐dependent anisotropy in the 3D VSP data are the major accomplishments derived from our attention to detail in the above steps.

A multioffset, three‐component VSP study in the Sudbury Basin

A multioffset, three‐component vertical seismic profiling (VSP) experiment was carried out in the Sudbury Basin, Ontario, as a part of the LITHOPROBE Sudbury Transect. The main objectives were determination of the shallow velocity structure in the middle of the Sudbury Basin, development of an effective VSP data processing flow, correlation of the VSP survey results with the surface seismic reflection data, and demonstration of the usefulness of the VSP method in a crystalline rock environment. The VSP data processing steps included rotation of the horizontal component data, traveltime inversion for velocity analysis, Radon transform for wavefield separation, and preliminary analysis of shear‐wave data. After wavefield separation, the flattened upgoing wavefields for both P‐waves and S‐waves display consistent reflection events from three depth levels. The VSP-CDP transformed section and corridor stacked section correlate well with the high‐resolution surface reflection data. In addition to obtaining realistic velocity models for both P‐ and S‐waves through least‐square inversion and synthetic seismic modeling for the Chelmsford area, the VSP experiment provided an independent estimation for the reflector dip using three component hodogram analysis, which indicates that the dip of the contact between the Chelmsford and Onwatin formations, at an approximate depth of 380 m in the Chelmsford borehole, is approximately 10.5° southeast. This study demonstrates that multioffset, three‐component VSP experiments can provide important constraints and auxiliary information for shallow crustal seismic studies in crystalline terrain. Thus, the VSP technique bridges the gap between the surface seismic‐reflection technique and well‐log surveys.