Vertical Seismic Profile Data Research Articles

Inversion of fracture weakness parameters based on 3C vertical seismic profile data — Part I: Horizontal transversely isotropic media composed of a single fracture set in the isotropic background media

Natural fractures in oil and gas reservoirs are a crucial factor that cannot be ignored because they significantly influence the reservoir’s petrophysical properties and hydrocarbon development. A horizontal transversely isotropic (HTI) medium composed of a single fracture set in an isotropic background is a typical anisotropic medium. Meanwhile, the S-wave splitting is a sensitive response of such anisotropic media, resulting in the generation of fast and slow S waves. The normal and tangential fracture weaknesses are crucial parameters that characterize the anisotropy of fractured media. We develop an inversion method for fracture weakness based on 3C vertical seismic profiling (3CVSP) data. First, assuming weak anisotropy and an HTI medium containing a single fracture set, we derive a first-order linear approximation of the traveltimes of the converted fast and slow S waves (PS1 and PS2 waves) with respect to fracture weakness parameters in the phase velocity domain. By solving for the horizontal projection of the slowness vector, approximate equations of the traveltimes of the PS1 and PS2 waves are converted from the phase velocity domain to the group velocity domain. Furthermore, we devise an inversion workflow consisting of three primary steps: (1) preprocessing the VSP data to derive the traveltimes and azimuth of the HTI medium, (2) constructing a forward model with undetermined fracture weakness parameters, and (3) following the establishment of the objective function, conducting the inversion for the fracture weakness parameters. We demonstrate the reliability of the method through numerical examples and synthetic 3CVSP data. The inversion errors are primarily influenced by the azimuth angle, with minimal influence from the receiver depth. Furthermore, a collective set of inverted results derived from all geophones is more stable and accurate than individual geophones. The application to actual 3CVSP data further confirms the effectiveness of our approach.

Separation of fast and slow shear waves and prediction of fracture parameters based on non-orthogonal assumptions

SUMMARY Natural fractures play a significant role in oil and gas reservoirs. Accurate predictions of fracture parameters are vital in reservoir prediction and oil and gas development. The birefringent phenomenon of shear waves in fractured media makes shear wave splitting (SWS) analysis an important tool in formulating fracture predictions. The traditional SWS analysis method is based on an orthogonal assumption of fast and slow shear waves. However, in an orthotropic medium composed of a background vertical transversely isotropic medium and a set of vertical fractures, fast and slow shear waves are not necessarily orthogonal. This causes the traditional SWS analysis method to fail. To solve this problem, we proposed an SWS analysis algorithm with a non-orthogonal assumption of fast and slow shear waves in this study. First, we introduced a parameter (difference angle) to characterize the angle between slow shear waves and the normal polarization directions of the fast shear waves. Subsequently, based on the traditional two-parameter scanning algorithm, a parameter was added to facilitate three-parameter scanning. In addition, we derived an expression for the two-parameter scanning objective function using the non-orthogonal assumption. Two-parameter scanning can accurately extract fast and slow wave time delay data, but it cannot determine an accurate fast shear wave polarization direction. Therefore, we optimized the three-parameter scanning algorithm as follows: first, we used two-parameter scanning to obtain the fast and slow wave time delays and then performed further scanning to determine the polarization direction of the fast shear wave and difference angle. The optimization algorithm significantly improved the computational efficiency. Subsequently, we tested the accuracy of this method using synthetic single-trace and three-component vertical seismic profile data. We demonstrated the implementation process of the three-parameter scanning method using actual data, separated fast and slow shear waves, and predicted fracture parameters. The final fracture parameters were verified.

Estimation of rock physics properties via full waveform inversion of vertical seismic profile data recorded by accelerometer and fibre-optic sensors

SUMMARY Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada’s Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution.

Quality Control of DAS VSP Data in Desert Environment Using Simulations and Matching Filters.

The unconsolidated near surface and large, daily temperature variations in the desert environment degrade the vertical seismic profiling (VSP) data, posing the need for rigorous quality control. Distributed acoustic sensing (DAS) VSP data are often benchmarked using geophone surveys as a gold standard. This study showcases a new simulation-based way to assess the quality of DAS VSP acquired in the desert without geophone data. The depth uncertainty of the DAS channels in the wellbore is assessed by calibrating against formation depth based on the concept of conservation of the energy flux. Using the 1D velocity model derived from checkshot data, we simulate both DAS and geophone VSP data via an elastic pseudo-spectral finite difference method, and estimate the source and receiver signatures using matching filters. These field geophone data show high amplitude variations between channels that cannot be replicated in the simulation. In contrast, the DAS simulation shows a high visual similarity with the field DAS first arrival waveforms. The simulated source and receiver signatures are visually indistinguishable from the field DAS data in this study. Since under perfect conditions, the receiver signatures should be invariant with depth, we propose a new DAS data quality control metric based on local variations of the receiver signatures which does not require geophone measurements.

Marchenko imaging assisted by vertical seismic profiling data for land seismic data in the Middle East

Attenuating interference from internal multiples has challenged seismic data imaging in the Middle East basins. The challenge results from the strong short-period internal multiples that exhibit nearly indistinguishable characteristics from the primaries reflected from the underlying reservoirs due to predominantly horizontal strata and occasional low-relief structures, as indicated in the Jurassic formations in Kuwait. To address the internal-multiple issues, multiple prediction followed by adaptive subtraction is the most common approach in the industry. However, due to the similarities between primaries and multiples, applying adaptive subtraction poses a high risk of primary-amplitude damage, preventing quantitative seismic data interpretation. Therefore, we examine the Marchenko method that retrieves Green’s functions from surface seismic data for target-oriented imaging without applying adaptive subtraction. Marchenko imaging has produced promising results on several offshore seismic data sets, but an onshore application is still needed. To better understand the effects of internal multiples and implement Marchenko imaging, we perform integrated analysis through well-log, vertical seismic profiling (VSP), and seismic data from a hydrocarbon field in Kuwait. In addition, we use VSP data to cross-check the retrieved Green’s functions and estimate the scaling factor of the Marchenko method. The results indicate that (1) the poor imaging at the center of the field is due to the destructive interference of internal multiples, (2) the reverberation of internal multiples between the evaporite formations of the overburden is the most likely candidate that affects the seismic images of the Jurassic reservoirs, (3) the retrieved Green’s functions conform to the recorded Green’s functions from VSP data, and (4) Marchenko imaging provides a means to improve the seismic images of the Jurassic formations in Kuwait.

Time-lapse attenuation variations using distributed acoustic sensing vertical seismic profile data during CO2 injection at CaMI Field Research Station, Alberta, Canada

For seismic monitoring of geologic [Formula: see text] storage, it is useful to measure time-lapse (TL) variations of seismic attenuation. Seismic attenuation directly connects to different petrophysical parameters within the [Formula: see text] storage complex. We have developed an approach to derive smooth time-variant amplitude spectra from seismic signals by using the sparse strongest signal peaks, and then measuring two different attributes (conventional Q factor and its “geometric” counterpart) characterizing the path effects of seismic attenuation from the smooth spectra. This approach is straightforward and does not require sophisticated algorithms or parameterization schemes. We apply this approach to TL distributed acoustic sensing (DAS) vertical seismic profile (VSP) data from the Field Research Station (FRS) [Formula: see text] injection project in southern Alberta, Canada. High-quality stacked reflection records are obtained from the baseline and monitor DAS VSP surveys at the FRS and TL attenuation-attribute differences are derived from these reflection records. TL variations of attenuation are observed within the injection zone at the FRS, which are interpreted as being related to the injected [Formula: see text]. Although there is always a significant trade-off between the accuracy and temporal resolution of the measured attenuation parameters, reliable attenuation measurements around the injection zone are still achieved with an in-use reflection signal of a sufficient length and bandwidth. Attenuation attributes measured from this approach can be an advantageous tool for monitoring the distribution and migration of [Formula: see text] plumes.

Development of a subsea distributed acoustic sensing acquisition system

Over the past few decades, distributed acoustic sensing (DAS) data acquisition has seen great improvements from better interrogators, engineered fiber, and lessons learned from subsea installation and acquisition. This has given us confidence that DAS cables can be installed in wells with subsea trees to be used as receivers for vertical seismic profile (VSP) seismic imaging. VSP imaging for deepwater fields has been demonstrated to provide better illumination and higher-frequency seismic data. Permanent DAS cable installation can be used to acquire highly repeatable time-lapse (4D) data. DAS cables have been installed in a number of subsea wells on two deepwater oil fields with the intention of covering the crest of these fields with high-frequency seismic data. A system has been developed to allow for DAS acquisition on these offshore subsea wells with long-distance tie backs using permanently installed interrogators on the floating platforms and engineered fiber in the wells. On each of these fields, a DAS cable has now been installed, and subsequently, a zero offset (ZO) DAS VSP has been acquired for verification and commissioning. These ZO DAS VSP acquisitions indicate high-fidelity installations resulting in DAS VSP data with excellent data quality. These first subsea DAS acquisitions indicate great promise, and further installations and acquisitions are planned with the ultimate goal of providing high-frequency seismic images over the crest of these fields to reduce the uncertainty in decisions around reservoir management and future infill drilling.

An effective Q extraction method via deep learning

Abstract The quality factor (Q) is a parameter reflecting the physical properties of reservoirs. Accurate estimation of the quality factor plays an important role in improving the resolution of seismic data. Spectral-ratio method is a widely used traditional method based on the linear least-squares fitting to extract the quality factor, but is sensitive to noise. This is the main reason preventing this method from being widely used. Some supervised deep-learning methods are proposed to extract Q in which the construction of training labels is a key link. The proposed method is based on the spectral-ratio method to create training labels, avoiding errors in generating them. In contrast to the least-squares method, this paper proposes to use a nonlinear regression algorithm based on a fully connected network to fit the spectral logarithmic ratio and frequency. Meanwhile, the empirical equation is applied to constrain prediction results. The proposed method can effectively overcome the influence of noise and improve the accuracy of prediction results. Tests on the synthesized data of vertical seismic profile and common middle profile show that the proposed method has better generalization ability than the spectral-ratio method. Applying the method to the field vertical seismic profile data successfully extracts the quality factor, which can provide effective information for dividing stratigraphic layers.

Diffusion Model for DAS-VSP Data Denoising.

Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model's effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models' potential for DAS processing.

Field assessment of elastic full-waveform inversion of combined accelerometer and distributed acoustic sensing data in a vertical seismic profile configuration

Seismic data are a significant facilitator for monitoring in carbon capture and sequestration projects, providing high-resolution images of fluid migration, using, for example, full-waveform inversion (FWI). Distributed acoustic sensing (DAS), a relatively novel technology for wavefield sampling, is well suited for this type of monitoring. Using noninvasive optical fibers, DAS allows for dense spatial sampling along the entire length of the wellbore, without disrupting operations. Permanently installed in the wellbore, typically behind casing, DAS offers highly repeatable and dense sampling of the transmitted wave modes crucial to seismic monitoring of injected carbon dioxide ([Formula: see text]). However, the DAS data consist of measurements of strain along the tangent of the fiber and therefore do not transfer directly to conventional FWI algorithms. Incorporation of DAS data in their native strain (or strain-rate) form in standard FWI algorithms, requires changing the definition of the receiver sampling operator to use geometric information about the fiber to supply tangential strain measurements to the FWI residual. The theoretical developments are applied to invert field vertical seismic profile data acquired with DAS fiber and accelerometers at a [Formula: see text] sequestration site in Newell Country, Alberta. Our method incorporates DAS data and accelerometer data in one objective function and allows us to tune the relative importance we wish to place on each data set. This method also transfers to noncollocated sensors, for example, surface-deployed geophones and borehole fiber. The inverted models contain features expected from the geology of the field site, and data modeled in the inverted models compare favorably with the field data for these sensor types. The models are derived from data acquired prior to [Formula: see text] injection, representing baseline models for future time-lapse studies planned at the field research station.

Complete perception self-attention network for weak seismic signal recovery in distributed acoustic sensing vertical seismic profile data

Distributed acoustic sensing (DAS) is a new technology for acquiring seismic data with high spatial resolution at low cost. Furthermore, in real downhole seismic exploration, DAS can receive some weak signals reflected from deep and thin layers. Unfortunately, some real downhole seismic data received by DAS often are characterized by low quality. Specifically, in real DAS records, desired signals with weak energy often are contaminated by some new noise not presented in seismic data received by conventional electronic geophones. Due to the characteristics of seismic data, such as frequency band aliasing, low signal-to-noise ratio, and complex noise wavefield, existing linear or nonlinear denoising methods based on information processing theories cannot effectively eliminate this complex and multitype noise. Recently, the deep-learning method has been regarded as a powerful tool for background noise attenuation in seismic data. Most of the existing deep-learning methods are concerned with local features and ignore the global features that can be used to enhance their performance further. To simultaneously extract global and local features, we design a novel complete perception self-attention network (CP-SANet) based on the transformer framework and apply it to the denoising of downhole DAS records. The network embeds a transformer module into multilevel encoder-decoder framework. Depth-wise convolution is applied to enhance the local perception capability. Given the transformer’s requirement for a large amount of data, we specifically design abundant seismic data samples using formation models with different parameters. The noise in the data sets is obtained from actual field DAS data. The effectiveness and feasibility of CP-SANet are verified on synthetic and field DAS records. All of the experimental results prove its satisfactory performance compared with some classical and network methods.

An efficient deep learning method for VSP wavefield separation: A DAS-VSP case

The quality of wavefield separation on vertical seismic profiling (VSP) data directly affects subsequent imaging and inversion processes. However, the traditional methods have various defects in separating upgoing and downgoing waves. The application of deep learning brings another train of thought to solve related problems. The traditional methods, such as f-k filtering and Radon transform, produce results with spatial aliasing and inaccurate amplitudes. The median filtering method relies on accurate first-break picking and waveform consistency. Moreover, the results of traditional methods are subject to manual intervention. To overcome these problems, a deep-learning method is developed for the automatic wavefield separation of VSP data. First, the traditional Radon transform method is used to produce training data sets. Then, to ensure amplitude preservation and suppress spatial aliasing, new input data is formed by recombining the upgoing and downgoing waves extracted by Radon transform. The developed deep-learning network is highly efficient, and its accuracy exceeds that of the traditional methods only after one epoch training. A practical workflow of wavefield separation based on deep learning is established, and it is applied to synthetic data and field distributed acoustic sensing VSP data. The results indicate that our method is superior in terms of amplitude preservation and spatial aliasing suppression. The time consumption of our method is very acceptable and can be further minimized by training the network using downsampling data.

Inversion of walkaway VSP data in the presence of lateral velocity heterogeneity

Multi-azimuth walkaway vertical seismic profiling is an established technique for the estimation of in situ slowness surfaces and inferring anisotropy parameters. Normally, this technique requires the assumption of lateral homogeneity, which makes the horizontal slowness components at depths of downhole receivers equal to those measured at the surface. Any violations of this assumption, such as lateral heterogeneity or nonzero dip of intermediate interfaces, lead to distortions in reconstructed slowness surfaces and, consequently, to errors in estimated anisotropic parameters. In this work, we relax the assumption of lateral homogeneity and discuss how to correct vertical seismic profile data for weak lateral heterogeneity. We describe a procedure of downward continuation of recorded traveltimes that accounts for the presence of both vertical inhomogeneity and weak lateral heterogeneity, which produces correct slowness surfaces at depths of downhole receivers, noticing that sufficiently dense receiver coverage along a borehole is required to separate influences of vertical and lateral heterogeneity on measured traveltimes and obtain accurate estimates of the slowness surfaces. Once the slowness surfaces are found and a desired type of anisotropic model to be inverted is selected, the corresponding anisotropic parameters, providing the best fit to the estimated slownesses, can be obtained. We invert the slowness surfaces of P-waves for parameters of the simplest anisotropic model describing dipping fractures (transversely isotropic medium with a tilted symmetry axis). Five parameters of this model, namely, the P-wave velocity V0 in the direction of the symmetry axis, Thomsen's anisotropic coefficients ϵ and δ, the tilt ν, and the azimuth β of the symmetry axis, can be estimated in a stable manner when maximum source offset is greater than half of receiver depth.

Interpretable denoising of distributed acoustic sensing vertical seismic profile data using adaptive consistent prior net

Distributed Acoustic Sensing (DAS) is vital in seismic exploration, especially Vertical Seismic Profile (VSP) studies, due to its high sensitivity, fine velocity modeling, and resistance to harsh conditions. However, DAS VSP data often suffer from low quality due to complex noise sources. Previous studies show that convolutional neural network (CNN) denoising methods can achieve good results, but they often lack interpretable mechanisms for denoising, posing a challenge in understanding and interpreting the results. To overcome these limitations, we propose an adaptive consistency prior network (ACPNet) algorithm, based on a model-guided design. First, we introduce an adaptive consistency prior algorithm to form a model-based denoising method that guides the network design, leading to an end-to-end trainable and interpretable denoising network. We constructed a comprehensive training dataset, and the network is trained using the L1 loss function to achieve effective signal-to-noise separation. The combined synthetic data and field data denoising results demonstrate that the proposed method can effectively suppress various types of noise in DAS-VSP data and has a strong signal recovery capability. Furthermore, the denoising performance of the proposed method surpasses that of traditional methods and several deep learning techniques.

Geophysical Constraints to Reconstructing the Geometry of a Shallow Groundwater Body in Caronia (Sicily)

The characterization of a groundwater body involves the construction of a conceptual model that constitutes the base knowledge for monitoring programs, hydrogeological risk assessment, and correct management of water resources. In particular, a detailed geological and geophysical approach was applied to define the alluvial Caronia Groundwater Body (CGWB) and to reconstruct a hydrogeological flow model. The analysis of the CGWB, located in north-eastern Sicily, was initially approached through a reanalysis of previous stratigraphic (boreholes) and geophysical (vertical electrical soundings and seismic refraction profiles) data, subsequently integrated by new seismic acquisitions, such as Multichannel Analysis of Surface Waves (MASW) and horizontal-to-vertical seismic ratio (HVSR). The analysis and reinterpretation of geoelectrical data allowed the construction of a preliminary 3D resistivity model. This initial modeling was subsequently integrated by a geophysical data campaign in order to define the depth of the bottom of the shallow CGWB and the thickness of alluvial deposits. Finally, a preliminary mathematical model flow was generated in order to reconstruct the dynamics of underground water. The results show that integration of multidisciplinary data represent an indispensable tool for the characterization of complex physical systems.

Deep learning-based upgoing and downgoing wavefield separation for vertical seismic profile data

The vertical seismic profile (VSP) considerably aids attenuation analysis and velocity calibration, enabling high-resolution seismic exploration. However, the imaging and interpretation of VSP data require pure upgoing and downgoing wavefields, and their separation is critical in processing and interpretation. Often, achieving high precision and efficient wavefield separation can be regarded as a challenging issue. To automate this process with higher accuracy, we develop a deep-learning high-precision intelligent separation method. First, to construct a physics-driven and data-generic training set, the well-log data from different geologic environments are used to simulate the upgoing and downgoing VSP data independently. Thereafter, we train a multitask-learning neural network to regress the network weights before and after separation to tune a mapping model, which is used to implement intelligent upgoing and downgoing wavefield separation. Validation data confirm the feasibility of our method. To further study the generalizability and robustness of the method, we apply the trained model to separate the modeled and real VSP data. By analyzing the separated profiles and frequency-wavenumber spectra, it is observed that the intelligent separation method is superior to conventional methods with a balance between accuracy and efficiency. It can be concluded that our method can separate upgoing and downgoing wavefields in VSP data with excellent performance, even in complex geologic environments. Our study indicates that the multitask-learning neural network combined with a physics-driven and data-generic training set is a new strategy to separate upgoing and downgoing wavefields and deserves to be popularized and applied.

Construction of pseudo-Hessian for vertical seismic profile full waveform inversion

The pseudo-Hessian matrix has been widely developed and applied to full waveform inversion to reduce the computational cost of obtaining the Hessian and its inverse matrix. Because the conventional pseudo-Hessian matrix assumes a surface seismic profiling geometry, this conventional method is compatible only with horizontally deployed sources and receiver geometries and cannot reflect the geometrical spreading effect of vertically deployed receivers. To enable the pseudo-Hessian to properly consider vertically deployed receiver locations and the geometrical spreading inherent in vertical seismic profiling data, we re-formulate part of the pseudo-Hessian matrix formulation. We verify our new scheme by directly calculating the Hessian matrices for a simple 1D model and conducting inversion tests with synthetic data sets. A comparison to the approximated Hessian matrix confirms that the vertical seismic profiling pseudo-Hessian provides better agreement with the approximated Hessian than the conventional pseudo-Hessian. In addition, we describe two numerical tests using multi-offset synthetic vertical seismic profiling data sets. When we apply the conventional pseudo-Hessian matrix to invert the synthetic data, we find that the inversion process cannot properly invert far offset data. However, we find that the pseudo-Hessian regarding vertical seismic profiling geometry allows the inversion process to invert far offset data and produces fewer artifacts than the conventional method. This study demonstrates that the vertical seismic profiling pseudo-Hessian matrix represents an alternative method that is necessary for the calculation of the pseudo-Hessian matrix for full waveform inversion with vertical seismic profiling using the Gauss–Newton method.

Inverse Q-Filtering as a Tool for Seismic Resolution Enhancement: A Case Study from the Carpathian Foredeep

In this study, we analyze the quality factor- Q derived from the Vertical Seismic Profiling (VSP) and sonic log data that was performed in the DS-2 borehole located in the Carpathian Foredeep. We use the obtained quality factor distributions in the inverse Q-filtering (IQF) procedure during active seismic data processing. Another very important and, unfortunately, subjective in nature parameter is the gain function applied to seismic data while performing the IQF. To calculate Q from VSP data we use an iterative approach to the spectral ratio method where we use several shallowest receivers as reference. We observe high variability of the obtained Q values, depending on the reference receiver, however, depth distribution of Q was consistent regardless To increase reliability of the obtained values, an additional method of Q measurements is incorporated, i.e., acoustic travel time dispersion method. We show that incorporating inverse Q-filtering to active seismic data processing flow allows to enhance the vertical resolution of 2D seismic data.

Finite-frequency sensitivity kernels and hierarchical traveltime and waveform inversion of direct and reflected waves from vertical seismic profile data

Wave-equation-based traveltime or waveform inversion updates the velocity model along finite-frequency sensitivity kernels and exhibits higher accuracy than ray-based tomography. Owing to special geometry, the kernels for vertical seismic profile (VSP) data are different from those for surface seismic data. We have used the Born approximation to compute the traveltime and waveform kernels for direct and reflected waves from VSP data. Based on the property of sensitivity kernels for different information and arrivals, we develop a hierarchical inversion scheme: joint wave-equation traveltime inversion (JWETI) of direct and reflected waves, joint waveform inversion (JWI) of direct and reflected waves, and full-waveform inversion (FWI) of the reflected wave. The traveltime kernels, the waveform kernels, and the migration isochrones are used to retrieve the long-, intermediate-, and short-wavelength components of the velocity model, respectively. Numerical tests on synthetic and real walkway VSP data reveal that the JWETI stage obtains a plausible velocity macromodel, which can be treated as a starting model for subsequent JWI and FWI stages. The JWI stage is a transition between JWETI and FWI and helps to improve the structures of the deep layers of the model. The FWI stage offers some high-wavenumber contents to the inverted model. For the VSP survey, the transmitted direct waves can be used to reduce the dependence of FWI on the initial model. However, the conventional reflection-based wave-equation traveltime inversion and FWI still suffer from cycle skipping when the initial model is far away from the real model. In contrast, the hierarchical inversion scheme can yield reasonable inversion results around the borehole, even when large velocity errors are present in the starting model.

Coupled Hydromechanical Modeling and Assessment of Induced Seismicity at FWU: Utilizing Time-Lapse VSP and Microseismic Data

The objective of this work is to utilize integrated geomechanics, field vertical seismic profile (VSP) and microseismic data to characterize the complex subsurface stress conditions at the Farnsworth Unit (FWU). The model is based on a five-spot sector model extracted from a primary geomechanical model. The five-spot well injection pattern is characterized by extensive reservoir characterization data, such well logs, extracted cores and borehole geophone data, to facilitate the detailed examination of stress changes and microseismic event occurrences. The study utilizes field vertical seismic volumes acquired from the injection well 13-10A. The seismic volumes successfully provided snapshots of the behavior of the reservoir at distinct times. The use of VSP and microseismic data provided direct and indirect estimates of the dynamic stress changes occurring in the overburden, reservoir and underburden rock formations. In order to illuminate the stress regions and identify rocks that have undergone inelastic failure, microseismic event occurrences were utilized. Microseismic activity has been detected at the FWU; further study of its locations, timing, and magnitude was needed to deduce the nature of the changing stress state. The results of the study revealed that microseismic events were successfully modeled within the Morrow B formation. Moment magnitudes of seismic events were within the same magnitudes for events in the reservoir, suggesting the suitability of the model. The results of the study showed that the computed moment magnitudes for seismic events were insignificant to warrant safety concerns. The study findings showed the usefulness of coupled hydromechanical models in predicting the subsurface stress changes associated with CO2 injection. The knowledge gained from this study will serve as a guideline for industries planning to undertake underground CO2 storage, and characterize the subsurface stress changes.