Downhole Microseismic Data Research Articles

Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network

Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network

Seismic Anisotropy Estimation Using a Downhole Microseismic Data Set in a Shale Gas Reservoir

Shale anisotropy has a significant impact on the data processing and interpretation of microseismic monitoring in shale gas reservoirs. A geology- and rock-physics-constrained approach to estimating shale anisotropy using down-hole microseismic data sets is proposed in this study and is applied to the case of Horn River shale. A priori knowledge of shale anisotropy is obtained by integrating geological analyses and rock physics studies. This knowledge serves as an important constraint when building the initial model, minimizing the uncertainties and evaluating the results. The application to Horn River shale shows that the optimized anisotropic velocity model reduces the time misfit by about 65% compared to the originally provided velocity model. As the relocated perforation shot indicates, the event locations are significantly improved. The results also show that a high fraction of clay mineral results in strong fabric anisotropy in the Fort Simpson formation, whereas the quartz-rich shale gas reservoirs (Muskwa and Otter Park formations) show weaker fabric anisotropy. The percentage of velocity anisotropy in Horn River shale can be up to 40%. The fabric anisotropy of shale derived from the downhole microseismic data set is comparable with that of laboratory experiments. This demonstrates that downhole microseismic monitoring, as a quasi in situ experiment, has the potential to contribute to a better understanding of subsurface anisotropy beyond the laboratory. In addition, microseismic measurements of shale anisotropy are conducted in the seismic frequency band and are thus more applicable for further seismic applications.

Downhole microseismic data interpretation for media anisotropy evaluation with limited acquisition geometry in Western Siberia

We have evaluated the results of processing and interpretation for one of the first projects in Russia for downhole microseismic monitoring of hydraulic fracturing in Western Siberia. The data are characterized by large distance to the acquisition array and noisy recordings. We illustrate that preliminary quality control of microseismic array installation is a useful tool that may predict some of the issues and help to promote better acquisition quality. Therefore, it is important to reiterate the significance of the supervision for microseismic field measurements, especially when the technology is tested in new regions. Despite limited acquisition aperture and a comparatively small number of observed microseismic events, it is possible to derive valuable interpretation results from the data set. The geometry of the event locations suggests significant unplanned development of the hydraulic fractures, including a significant shift of the fracture from the initiation point. Records with clear S-wave splitting allowed us to determine arrival times of P waves and two S waves for several microseismic events. Arrival times were then used for simultaneous kinematic inversion for microseismic-event location and anisotropic velocity-model update (layered transversely isotropic with the vertical axis of symmetry model). We estimate significant anisotropy from microseismic data. Thomsen parameters [Formula: see text] and [Formula: see text] are well constrained, whereas parameter [Formula: see text] is constrained only for one of the layers.

Phase arrival picking for bridging multi-source downhole microseismic data using deep transfer learning

AbstractThe phase arrival picking of the downhole microseismic dataset is a critical step in fracturing monitoring data processing. Recently, data-driven methods have been widely used in seismology studies, especially in seismic phase picking. The picking results heavily depend on whether large quantities of accurately labeled phase samples could be obtained to extract the characteristics of seismic waveforms. Also, there is a shortcoming of poor generalization ability in dealing with the cross-source transfer scenarios. In this paper, we propose a novel deep transfer learning method for microseismic phase arrival picking by fine-tuning one existing pretrained model based on a few phase samples. The pretrained model, which has been domain-adapted for phase picking, adopts 2D U-Net to both extract time and space features, thereby improving the overall picking accuracy. Moreover, the fully convolutional U-Net architecture has the ability to handle samples with variable sizes so could be used for bridging downhole microseismic data from different sources. The results of two transfer cases show that compared with the direct application of the pretrained model and a newly trained model, the proposed method could provide more satisfactory performance with only limited seismic phase samples. Also, our method significantly reduces the cost of labeling and saves time because of avoiding repeated training.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning

Microseismic data usually have a low signal-to-noise ratio, necessitating the application of an effective denoising method. Most conventional denoising methods treat each component of multicomponent data separately, e.g., denoising methods with sparse representation. However, microseismic data are often acquired with a 3C receiver, especially in borehole monitoring cases. Independent denoising ignores the relative amplitudes and vector relationships between different components. We have developed a new simultaneous denoising method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint with simultaneous transformation of 3C data. The joint sparse optimization problem is solved using the extended orthogonal matching pursuit. Synthetic microseismic data with a double-couple source mechanism and two field downhole microseismic data were used for testing. Independent denoising of 1C data with the fixed dictionary method and simultaneous denoising of 3C data with the fixed dictionary and dictionary learning (3C-DL) methods were compared. The results indicate that among the three methods, the 3C-DL method is the most effective in suppressing random noise, preserving weak signals, and restoring polarization information; this is achieved by combining the time consistency constraint and dictionary learning.

3D waveform inversion of downhole microseismic data for transversely isotropic media

ABSTRACT3D anisotropic waveform inversion could provide high‐resolution velocity models and improved event locations for microseismic surveys. Here we extend our previously developed 2D inversion methodology for microseismic borehole data to 3D transversely isotropic media with a vertical symmetry axis. This extension allows us to invert multicomponent data recorded in multiple boreholes and properly account for vertical and lateral heterogeneity. Synthetic examples illustrate the performance of the algorithm for layer‐cake and ‘hydraulically fractured’ (i.e. containing anomalies that simulate hydraulic fractures) models. In both cases, waveform inversion is able to reconstruct the areas which are sufficiently illuminated for the employed source‐receiver geometry. In addition, we evaluate the sensitivity of the algorithm to errors in the source locations and to band‐limited noise in the input displacements. We also present initial inversion results for a microseismic data set acquired during hydraulic fracturing in a shale reservoir.

Signal-to-noise ratio enhancement for 3C downhole microseismic data based on the 3D shearlet transform and improved back-propagation neural networks

As the seismic responses of unconventional hydraulic fracturing, downhole microseismic signals play an essential role in the exploitation of unconventional oil and gas reservoirs. In geologic structure interpretation and reservoir development, high-quality downhole microseismic data are necessary. However, the characteristics of downhole microseismic signals, such as weak energy and high frequency, bring great difficulty to signal-to-noise ratio enhancement. How to suppress the random noises in 3C downhole microseismic signals becomes problematic. To solve this problem, the 3D shearlet transform is introduced into downhole microseismic data processing. Different from the 2D shearlet transform, the correlation among the 3C of downhole microseismic signals is fully considered in the 3D shearlet transform, which enables the 3D shearlet transform to suppress random noise more effectively. In addition, for accurate selection of 3D shearlet coefficient, the back-propagation (BP) neural network is applied to the selection of coefficients. Unlike conventional threshold functions, BP neural networks can achieve optimal results by repeated training. At the same time, a new weight factor is proposed to improve the misconvergence of BP neural networks. Experimentally our method has been used to process synthetic and real 3C downhole microseismic signals, with results indicating that, compared with conventional methods, our new algorithm exhibits better performance in valid signal preservation and random noise suppression.

Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

Downhole microseismic data has the significant advantages of high signal‐to‐noise ratio and well‐developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy. In this study, we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system. Synthetic tests inidcate that the objective function of locations can be defined as a multi‐dimensional matrix space by employing the global optimization DIRECT algorithm, because it can be run without the initial value and objective function derivation, and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension. This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions. Therefore, the methodology, based on a multidimensional DIRECT algorithm, can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution, which is more suitable for real‐time processing of a large amount of downhole microseismic monitoring data.

Signal-to-noise ratio enhancement for downhole microseismic data based on 3D shearlet transform

Downhole microseismic data are characterized for their high frequency and small amplitude, which bring great difficulty for noise suppression. We present a random noise attenuation method for downhole microseismic data based on the 3D shearlet transform (3DST). In contrast to the 2D shearlet, 3DST takes into account the correlation among three components of downhole microseismic. With the help of correlation among the data, downhole microseismic data are reassembled into a new 3D matrix and then transformed to the shearlet domain. After the analysis of the coefficients’ energy and the high-order cumulant on each scale, an efficient threshold function is proposed. We apply a small threshold to the coefficients associated with the signal’s scales, and a large threshold is chosen for the scales of the noise. Experimental results indicate that the algorithm significantly improves the signal-to-noise ratio of the microseismic data and effectively preserves a valid signal.

Automatic time picking of microseismic data based on shearlet-AIC algorithm

Time picking is a crucial step in microseismic data processing. The picking results have a great influence on the orientation of hypocenter location. Especially when the signal-to-noise ratio (SNR) of data is low, it is difficult to obtain arrival times accurately with conventional approaches. To solve the question above, this paper proposes a new time-picking approach based on the Akaike Information Criterion (AIC) and shearlet transform named the shearlet-AIC, which can accurately pick the arrival times. With the proposed method, the downhole microseismic data can be divided into several scales according to the different statistical characteristic between signals and noise and obtain the feature of a different frequency domain. We can acquire the minimum values that represent the picking results in every scale of the frequency domain by using the shearlet-AIC. To verify the reliability of the proposed method, we conduct it on both synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that our method can precisely pick the arrival times of P-waves even when the SNR of data is as low as − 8 dB and the accuracy is superior to the other methods mentioned in our paper.

Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding

Downhole microseismic data have the characteristics of low signal-to-noise ratio and high frequency, which pose a major challenge to noise attenuation. In this paper, we propose a novel downhole microseismic denoising approach based on the empirical wavelet transform combined with adaptive thresholding. According to the frequency characteristics of the signal, a spectrum segmentation strategy is designed. It can adaptively decompose the signal and noise into different modes. Through analyzing the spectrum and energy of the modes, different threshold functions are applied. The mode that contains more valid signals is processed by hard thresholding to reserve the amplitude; the other modes, which contain less useful signals, are processed by modified threshold functions to maintain the continuity of the restructured signal. We have determined this approach's potential for microseismic denoising by comparing its performance on synthetic and field data using complete ensemble empirical mode decomposition, wavelet transform, and synchrosqueezed wavelet transform.

SNR enhancement for downhole microseismic data based on scale classification shearlet transform

Shearlet transform (ST) can be effective in 2D signal processing, due to its parabolic scaling, high directional sensitivity, and optimal sparsity. ST combined with thresholding has been successfully applied to suppress random noise. However, because of the low magnitude and high frequency of a downhole microseismic signal, the coefficient values of valid signals and noise are similar in the shearlet domain. As a result, it is difficult to use for denoising. In this paper, we present a scale classification ST to solve this problem. The ST is used to decompose noisy microseismic data into serval scales. By analyzing the spectrum and energy distribution of the shearlet coefficients of microseismic data, we divide the scales into two types: low-frequency scales which contain less useful signal and high-frequency scales which contain more useful signal. After classification, we use two different methods to deal with the coefficients on different scales. For the low-frequency scales, the noise is attenuated using a thresholding method. As for the high-frequency scales, we propose to use a generalized Gauss distribution model based a non-local means filter, which takes advantage of the temporal and spatial similarity of microseismic data. The experimental results on both synthetic records and field data illustrate that our proposed method preserves the useful components and attenuates the noise well.

Microseismic Monitoring of Stimulating Shale Gas Reservoir in SW China: 2. Spatial Clustering Controlled by the Preexisting Faults and Fractures

AbstractMicroseismic monitoring is crucial to improving stimulation efficiency of hydraulic fracturing treatment, as well as to mitigating potential induced seismic hazard. We applied an improved matching and locating technique to the downhole microseismic data set during one treatment stage along a horizontal well within the Weiyuan shale gas play inside Sichuan Basin in SW China, resulting in 3,052 well‐located microseismic events. We employed this expanded catalog to investigate the spatiotemporal evolution of the microseismicity in order to constrain migration of the injected fluids and the associated dynamic processes. The microseismicity is generally characterized by two distinctly different clusters, both of which are highly correlated with the injection activity spatially and temporarily. The distant and well‐confined cluster (cluster A) is featured by relatively large‐magnitude events, with ~40 events of M −1 or greater, whereas the cluster in the immediate vicinity of the wellbore (cluster B) includes two apparent lineations of seismicity with a NE‐SW trending, consistent with the predominant orientation of natural fractures. We calculated the b‐value and D‐value, an index of fracture complexity, and found significant differences between the two seismicity clusters. Particularly, the distant cluster showed an extremely low b‐value (~0.47) and D‐value (~1.35). We speculate that the distant cluster is triggered by reactivation of a preexisting critically stressed fault, whereas the two lineations are induced by shear failures of optimally oriented natural fractures associated with fluid diffusion. In both cases, the spatially clustered microseismicity related to hydraulic stimulation is strongly controlled by the preexisting faults and fractures.

High-resolution microseismic imaging

We examine the feasibility of high-resolution microseismic imaging of unconventional reservoirs. Given frequencies in typical downhole microseismic data that are almost an order of magnitude higher than in seismic reflection data, a comparable increase in image resolution might be expected, bringing potential resolution of downhole microseismic images to a few meters. We demonstrate that such a resolution is indeed achievable and present two case studies illustrating the reservoir features that can be imaged. Our first example, constructed with P-waves recorded in the Woodford Field, reveals internal fabric of the Woodford reservoir and formations surrounding it, allowing us to discriminate the stimulated and unstimulated zones of the Lower Woodford Shale. Encouraged by those results, in our second example, this time from the Bakken Field, we use the slow shear waves, which are more sensitive to fluids than P-waves, to find out whether hydraulic fractures themselves could be imaged. Our seismic volume contains peculiar geobodies, growing precisely from perforation holes spaced at a 40 ft interval in a treatment well landed in the Middle Bakken. These geobodies penetrate through the Lower Bakken Shale reservoir and terminate at the top of the Three Forks dolomites. While our interpretation of the extracted geobodies as hydraulic fractures remains an interpretation, the remarkably high resolution of seismic images obtained in both case studies is unquestionable.

Joint inversion for effective anisotropic velocity model and event locations using S-wave splitting measurements from downhole microseismic data

An accurate velocity model is essential in microseismic imaging. Layered sediments and subvertical cracks from tectonic stress and hydraulic fracturing make velocity models more complicated than isotropy or simple anisotropy, such as vertical transverse isotropy. Downhole microseismic acquisition usually cannot achieve sufficient ray coverage that is required to develop a low-symmetry anisotropic model from traveltimes alone. To solve this problem, we have developed a new type of data, S-wave splitting parameters (the delay time of the slow S-wave and fast S-wave polarization direction), to determine anisotropic models. A genetic algorithm inversion is adopted to solve for the locations of events and the stiffness tensor of an anisotropic medium simultaneously. We applied this method to synthetic waveforms from numerical modeling and successfully recovered the input event locations and the velocity model. The effectiveness of this method is further demonstrated by using real microseismic data acquired in the Bakken shale reservoir. Compared with the inversion with an isotropic velocity model, event locations from an anisotropic model become well-aligned with natural fractures in the Bakken Formation. Our experiments have evidenced that adding full S-wave splitting parameters makes a significant improvement in constraining a low-symmetry anisotropic model from downhole microseismic data.

Shear-wave splitting analysis of downhole microseismic data for reservoir characterization of the Montney Formation, Pouce Coupe, Alberta

Following the symbiotic emergence of hydraulic fracture stimulation and microseismic monitoring, the phenomenon of shear-wave splitting is now being measured from completions operations. This study focuses on the downhole microseismic survey acquired at Pouce Coupe during the hydraulic fracture treatment of three horizontal wells. Microseismic shear-wave splitting analysis was performed to better constrain the physical rock properties of the Montney Formation. A strong temporal correlation was also observed between shear-wave splitting measurements and completion data providing insight into the response of the reservoir during hydraulic stimulation. In recent years, significant strides have been made to improve and semi-automate a workflow to measure shearwave splitting from microseismic data. Multiple studies have previously exploited this approach to model seismic anisotropy and infer the strength and orientation of aligned fracture sets and sedimentary fabrics within producing reservoirs (Verdon et al., 2011).

A review and appraisal of arrival-time picking methods for downhole microseismic data

We have evaluated arrival-time picking algorithms for downhole microseismic data. The picking algorithms that we considered may be classified as window-based single-level methods (e.g., energy-ratio [ER] methods), nonwindow-based single-level methods (e.g., Akaike information criterion), multilevel- or array-based methods (e.g., crosscorrelation approaches), and hybrid methods that combine a number of single-level methods (e.g., Akazawa’s method). We have determined the key parameters for each algorithm and developed recommendations for optimal parameter selection based on our analysis and experience. We evaluated the performance of these algorithms with the use of field examples from a downhole microseismic data set recorded in western Canada as well as with pseudo-synthetic microseismic data generated by adding 100 realizations of Gaussian noise to high signal-to-noise ratio microseismic waveforms. ER-based algorithms were found to be more efficient in terms of computational speed and were therefore recommended for real-time microseismic data processing. Based on the performance on pseudo-synthetic and field data sets, we found statistical, hybrid, and multilevel crosscorrelation methods to be more efficient in terms of accuracy and precision. Pick errors for S-waves are reduced significantly when data are preconditioned by applying a transformation into ray-centered coordinates.

Radon transform-based microseismic event detection and signal-to-noise ratio enhancement

We present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P– and S–wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records.

Feasibility of jointly locating microseismic events with data from surface and downhole receivers

Jaromir Jansky, Vladimir Plicka and Leo Eisner model the feasibility and potential advantages for hydraulic fracture mapping of using both surface and downhole microseismic data.