Downhole Microseismic Monitoring Research Articles

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.

Machine learning-assisted processing workflow for multi-fiber DAS microseismic data

In recent years, Distributed Acoustic Sensing (DAS) deployed in deviated wells has been increasingly used for microseismic monitoring. DAS can provide observations of microseismic wavefields with high spatial resolution and wide aperture, at the cost of unusually large data volumes compared with conventional downhole microseismic monitoring. To tackle this big-data challenge, we have developed key elements of a processing workflow that is assisted by machine learning techniques. We trained a convolutional neural network (CNN) for event detection and a U-Net model for both P- and S-wave arrival time picking. The workflow was applied to two multiwell DAS datasets acquired during hydraulic fracturing completions in western Canada. These datasets also include co-located 3C borehole geophone arrays that enable further comparison between catalogs from both sensor types. Compared with a traditional short-term average/long-term average (STA/LTA) method for event detection, our results indicate that the CNN method has a lower false-trigger rate and increases the event catalog size by a factor of 2.6–5.6. U-Net yields arrival-time picks with relatively small errors, high efficiency, and minimal user intervention, providing hypocenter location and focal depth that is arguably more accurate than the geophone catalog. While the proposed automated workflow requires substantial effort to build high-quality and large training datasets, it enables the use of DAS for real-time seismicity monitoring and risk management after the training stage. Although the DAS system detected fewer events than the geophone catalog and missed smaller magnitude events, our results indicate that fiber-optic sensors provide enough sensitivity to detect and locate sufficient events to characterize the treatment stages. DAS also captured induced events located at a hypocentral distance of >1 km, which are possibly indicative of reactivation of structural features.

Receiver orientation and event back-azimuth estimation for downhole microseismic monitoring using a probabilistic method based on P-wave polarization

Microseismic event back-azimuth is an indispensable parameter for source localization in downhole microseismic monitoring, and the accurate orientation of horizontal components of downhole seismic receivers is vital for reliably determining the event back-azimuth. Variation in the monitoring data quality may jeopardize the accuracy of receiver orientation which will further affect the event back-azimuth estimation. To mitigate this issue, we proposed a new probabilistic method based on P-wave polarization analysis for receiver orientation and event back-azimuth estimation. The algorithm constructs the von Mises distribution function using the polarization angle and corresponding rectilinearity of the P-wave, then determines the target angle using the maximum of the probability function. The receiver having the highest rectilinearity from the active-source event is used to quantify a reliable absolute orientation angle, and the relative orientation angles are calculated by the probability distributions based on the measurement angle differences and the associated averages of rectilinearity from all events. After receiver orientation, the P-wave polarization angles with different rectilinearity values are applied to construct the probability distribution functions to estimate the event back-azimuths. By using high-quality events and multi-receiver recordings, our methodology can greatly reduce the unintentional error in receiver orientation and increase event back-azimuth accuracy. We investigate the feasibility and reliability of the proposed method using both synthetic and field data. The synthetic data results demonstrate that, compared to the conventional methods, the proposed method can minimize the variance of the receiver orientation angle and back-azimuth estimation. The weighted standard deviation analysis demonstrates that the proposed method can reduce the orientation error and improve the event back-azimuth accuracy in the field dataset.

威荣气田深层页岩气压裂井中微地震监测技术

威荣气田深层页岩气压裂井中微地震监测技术

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.

Event localization results obtained by various processors in downhole microseismic monitoring and their analysis

The paper compares the processing results of four different companies for the same amount of data. The main focus of the work is related to assessing the quality of building a velocity model of the medium. It turned out that the performers used quite different models from each other, and, accordingly, received different results from each other on the example of one of the events. We analyzed the quality of localization of perforation events in these models and the quality of timing, both for perforations and for one of the microseismic events.

A hybrid workflow for updating 1D velocity model and microseismic event location

An accurate velocity model is critical for determining the microseismic event locations. Simultaneous inversion and alternating inversion are two common strategies for updating the 1D velocity model and estimating event locations in downhole microseismic monitoring. Numerical experiments demonstrate that the limited distribution of the receivers and the coupling between the event locations and the 1D velocity model may lead to an ill-conditioned problem for simultaneous inversion. Thus, a good initial estimate is critical to producing a stable solution. Moreover, the alternating inversion decouples event locations from the velocity model, but it converges to the optimal solution very slowly. In this study, we present a hybrid workflow to update both the 1D velocity model and microseismic event locations by combining two strategies. We first acquire an initial solution with the alternating inversion and then conduct the simultaneous inversion with the alternating result as the starting values. Numerical tests and field data are applied to demonstrate the effectiveness of the hybrid workflow.

Downhole Microseismic Monitoring Using FOSS and Its Field Test Comparison With Moving-Coil Geophone

We report downhole microseismic monitoring field test results of a fiber-optic-based seismic sensor (FOSS) array in a multistage hydraulic fracturing stimulation and present, for the first time, its systematic comparison with the conventional moving-coil geophone array deployed on-site. Perforation shots' analysis demonstrates that the FOSS has ~7.5 dB higher narrowband signal-to-noise ratio and, thus, 2.3 times smaller azimuth calibration error than the commercial moving-coil geophone. These benefits are mainly attributed to the intrinsic immunity to electromagnetic interference and a higher frequency resonance of FOSS. Field test results show that the FOSS can identify P- and S-waves of microseismic events, the temporal and spatial distributions of which are consistent with the geophone. In addition, the FOSS is found preferable to detect microseismic events with higher frequencies from some hundreds of Hz-to-kHz range. This superior ability contributes to distinct signatures in the identified P- and S-waves, including a shorter event duration and more concentrated frequency band, comparing to those collected by the geophone. The fracture interpretation results validate the application of fiber-optic seismic sensors on downhole microseismic monitoring.

3D seismic interpretation and fault slip potential analysis from hydraulic fracturing in the Bowland Shale, UK

The Bowland Shale Formation is one of the most promising targets for unconventional exploration in the United Kingdom, with estimated resources large enough to supply the country's entire natural gas consumption for 50 years. However, development of the Bowland Shale has stalled due to concerns over hydraulic-fracturing-induced seismicity. Only three wells have been drilled and hydraulic-fractured to date in the Bowland Shale, and all three have produced levels of seismicity of sufficient magnitude to be felt at the surface. Susceptibility to induced seismicity will be determined by the presence of critically stressed faults. However, such faults can go undetected in conventional interpretation of 2D or 3D seismic surveys if they are shorter than the resolution retrievable from a seismic survey, or if they have low (and in some cases even zero) vertical displacement. In such cases, the faults that cause induced seismicity may only be visible via microseismic observations once they are reactivated. To better identify fault planes from 3D seismic images, and their reactivation potential due to hydraulic fracturing, a high-resolution fault-detection attribute was tested in a 3D seismic survey that was acquired over the Preston New Road site, where two shale-gas wells were hydraulic-fractured in the Bowland Shale in 2018 and 2019, obtaining fault planes with lengths between 400 and 1500 m. Fault slip potential was then estimated by integrating the obtained faults with the formation's stress and pore pressure conditions (with the Bowland shale also being significantly overpressured), and several critically stressed faults were identified near the previously hydraulic fractured wells. However, the faults that induced the largest seismic events in the Preston New Road site, of c. 200 m in length for seismic events of magnitudes below 3.0 (as imaged with a multicomponent, downhole microseismic monitoring array deployed during the hydraulic-fracturing stimulations), could not be identified in the 3D seismic survey, which only mapped fault planes larger than 400 m in length.

Transdimensional simultaneous inversion of velocity structure and event locations in downhole microseismic monitoring

In downhole microseismic monitoring, the velocity model plays a vital role in accurate mapping of the hydraulic fracturing image. For velocity model uncertainties in the number of layers or interface depths, the conventional velocity calibration method has been shown to effectively locate the perforation shots; however, it introduces nonnegligible location errors for microseismic events, especially for complex geologic formations with inclinations. To improve the event location accuracy, we have exploited the advantages of the reversible jump Markov chain Monte Carlo approach in generating different dimensions of velocity models and adopted a transdimensional Bayesian simultaneous inversion framework for obtaining the effective velocity structure and event locations simultaneously. The transdimensional inversion changes the number of layers during the inversion process and selects the optimal interface depths and velocity values to improve the event location accuracy. The confidence intervals of the simultaneous inversion event locations estimated by Bayesian inference enable us to evaluate the location uncertainties in the horizontal and vertical directions. Two synthetic examples and a field test are presented to illustrate the performance of our methodology, and the event location accuracy is shown to be higher than that obtained using the conventional methods. With less dependence on prior information, our transdimensional simultaneous inversion method can be used to obtain an effective velocity structure for facilitating highly accurate hydraulic fracturing mapping.

Joint event location and velocity model update in real-time for downhole microseismic monitoring: A deep learning approach

Accurate event location in downhole microseismic monitoring depends largely on how accurate the velocity model is reconstructed. On the flipside, construction of an accurate velocity model is always an uphill task marred with numerous uncertainties. However, with proper inversion approach, both the model and the location of the events can be jointly evaluated. We propose, in this study, a deep learning approach for locating microseismic events and performing velocity model update, in real-time, efficiently and accurately. Both tasks were considered as a multi-dimensional and non-linear regression problem and a multi-layer two-dimensional (2D) convolutional neural network (CNN) was designed to perform the inversions. In training the neural network, low signal-to-noise ratio (SNR) synthetic microseismic data were used to mimic field data. Overall results indicate that the CNN is capable of learning the relationship between the microseismic waveform data and the events locations and update the velocity model, in real-time, to a high degree of precision compared to classical methods. The errors in the inversion results are less than a few percent. In addition, deep learning offers a number of benefits for automated and real-time microseismic event location and velocity model update, including minimal preprocessing, continuous improvement in performance as more training data is obtained, as well as low computational cost after the network has been trained.

Generalized neural network trained with a small amount of base samples: Application to event detection and phase picking in downhole microseismic monitoring

The deep-learning method has been successfully applied to many geophysical problems to extract features from seismic big data. However, some applications may not have sufficient available data to directly train a generalized neural network. We have applied data augmentation on a significantly small number of samples to train a generalized neural network for microseismic event detection and phase picking, which could be used in different project settings and areas. We use the U-Net architecture consisting of 2D convolutional layers to create the prediction function, and we map the waveforms recorded by using multiple receivers to the P/S arrival time labels; thus, the neural network can learn the P/S moveout features from multiple receivers. The training set is generated by simulating various realizations of the data based on 10 original samples from the beginning of a hydraulic fracturing stage. The trained neural network is then used to detect the events and pick the P/S phases from the continuous data for different stages and projects. A grid search from a precalculated traveltime table is performed to determine the event location after an event is detected. We build a real-time event detection and location workflow without human intervention by combining the neural network and grid search method, and we apply the workflow to a different stage from the training events and a completely independent project that the neural network has not encountered. The results indicate that microseismic events are successfully detected and located, and the picking performance of the neural network is superior to that of a traditional autoregression picker.

Relocating microseismicity from downhole monitoring of the Decatur CCS site using a modified double-difference algorithm

SUMMARYThe injection of CO2 at the Decatur carbon capture and storage site has generated significant microseismic activity, which occurs in distinct spatial clusters up to approximately 2.2 km from the primary injection well. Accurate and precise event locations are vital for the characterization of the microseismicity to help understand the reservoir response to the CO2 injection, whilst enabling the identification of minor faults and fractures below the resolution of conventional active seismic imaging. However, microseismic monitoring of fluid injection sites, such as Decatur, is often performed using a network of borehole sensors often from a single well. While these downhole sensors have excellent detection capabilities, their poor azimuthal coverage limits the ability to precisely determine event locations. We have developed a modified double-difference relocation algorithm suitable for both 1-D and 3-D velocity models, and which incorporates differential back azimuth observations to allow the benefits of the original double-difference algorithm to be applicable to a downhole microseismic monitoring setting. Applying the modified double-difference algorithm to the microseismicity at Decatur, we have successfully relocated 4293 events. The relocation included over 59 million observations for 757 285 event pairs, split across seven geographic regions. Despite the majority of observations being recorded in only two boreholes, with an almost identical azimuthal coverage, the results have shown to be reliable with significantly reduced residuals and low uncertainties associated with the final locations. We have analysed the residuals in terms of their association with each geographic region, data type, station and individual events, to fully appreciate their influence in the inversion and the fit of the data to the final set of event locations. For each region, the relocated seismicity has become less diffuse with improved clustering, and with newly visible linear features often orientated in a NE–SW direction. These results show the potential improvements that can be made to microseismic event locations recorded by a borehole network with a limited and variable azimuthal distribution.

Using microseismic events to improve the accuracy of sensor orientation for downhole microseismic monitoring

ABSTRACTEvent locations are essential for microseismic monitoring endeavours to map hydraulic fractures. For downhole monitoring, the event back‐azimuthal angle, which is commonly evaluated as the average of the P‐wave polarization angle at each sensor, is necessary to project the 2D location in the vertical plane determined by the traveltime information to the 3D space. The accuracy of the P‐wave polarization angle at each sensor depends on the signal‐to‐noise ratio of the waveforms, as well as relies on the accuracy of the sensor orientation. The conventional approaches of the sensor orientation require an event with known back‐azimuth angle, such as surface orientation shots, perforation shots, ball‐drop events or teleseismic events. In terms of availability, the signals of the perforation shots or ball‐drop events are the most widely used information. But they are usually characterized by a low signal‐to‐noise ratio. We propose a new method to incorporate high‐signal‐to‐noise ratio microseismic events with unknown back‐azimuth angles into the sensor orientation process. It can significantly improve the accuracy of the relative orientation angles among all of the sensors, and also improve the accuracy of the absolute orientation angles by linking the relative orientation angles to the sensors with the high‐signal‐to‐noise ratio waveforms of the perforation shots or ball‐drop events. We use both synthetic and field data to test the feasibility and reliability of the new method. The results reveal that the new method can reduce the uncertainty of the orientation angle and improve the accuracy of the microseismic location, compared with the conventional approach when utilizing a perforation shot or ball‐drop event alone.

Study on the relationship between well pattern and artificial fracture configuration in tight reservoir

In order to summarize the relationship between reasonably well pattern and artificial fracture in Fuyu reservoir which using the vertical well development, we determined the fracture network scale of overlay channel and a single channel sand body on the basis of the fracture network fracturing test and the downhole microseismic fracture monitoring data and annulus testing data. The reasonable fracture network sizes of different sand bodies were obtained by establishing a typical well fracture-matrix model and applying numerical simulation of the production of 16 different fracture networks in 5 years. Under the premise of established reasonable scale of the fracture network, it provides reference for the reasonably well pattern in the new area with similar reserves in geological conditions by the comparing of the 5-year oil production in 6 different well spacing.

Comparison between Distributed Acoustic Sensing and Geophones: Downhole Microseismic Monitoring of the FORGE Geothermal Experiment

AbstractWe compare the performance of a downhole distributed acoustic sensing (DAS) fiber-optic array with that of conventional geophones. The downhole collocated arrays are part of the Frontier Observatory for Research in Geothermal Energy (FORGE) geothermal experiment, in which stimulation of the rock volume in an enhanced geothermal system (EGS) causes microseismic events. The DAS acquisition system yields data sampled at every 1 m at 2000 samples per second for the entire length of the well, spanning to a depth of 985 m from the surface. Whereas single DAS channels are substantially noisier than geophones at the same location, their large number and spatial coherency allow for the application of effective array processing techniques. We follow a complete workflow for the fiber-optic array: velocity model building, event detection, event location, and magnitude estimation. Estimated velocity models agree well with sonic logging in a nearby well and map a granitic contact accurately. Detection performance is somewhat worse than geophones and yields magnitude completeness of −1.4 compared to −1.7 for geophones. Using a single vertical fiber array, we cannot retrieve the azimuth of the events relative to the well. However, we can very accurately estimate their depth and horizontal distance from the array. Magnitude estimation with DAS approaches geophone results to within a standard deviation of M=0.115 and negligible mean difference. The DAS processing results outperform a regional and local surface array, consolidated with a shallow borehole sensor. Although downhole geophones in the FORGE experimental layout performed better, DAS holds several critical practical benefits that were not demonstrated. Thanks to its heat resistance, it can be deployed much closer to the reservoir; fibers can be deployed along cased active wells, eliminating the need for a dedicated monitoring well; the permanently installed fiber can be used for years or decades. Therefore, we argue that DAS holds vast potential for long-term monitoring of EGS projects.

Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data

Traveltime-based methods depend on the accurate determination of the arrival times of seismic waves. They further benefit from information on the uncertainty with which the arrival times are determined. Among other applications, arrival-time uncertainties are used to weight data in inversion algorithms and to define the resolution of reconstructed velocity models. The most physically meaningful approaches for the estimation of arrival-time uncertainties are based on probabilistic formulations. The two approaches for the assessment of the lower bound of arrival-time uncertainties, the Cramér–Rao Bound (CRB) and the Ziv–Zakai Bound (ZZB), have been reviewed. The CRB determines the minimum-achievable estimation error under the assumption of a high signal-to-noise ratio (S/N) but underestimates said error for small S/N. The ZZB provides a better result for noisy data because it utilizes a priori information. The CRB and ZZB require knowledge of the spectral variance of the signal, which often is hard to determine in seismic experiments. Furthermore, both bounds assume additive white Gaussian noise (AWGN), which does not hold for seismic data. To overcome these problems, alternative expressions have been proposed, which yield comparable estimates as CRB and ZZB but are solely based on the S/N and the dominant period in the data. Moreover, a recipe to correct the S/N and account for the difference between the seismic noise and AWGN has been provided. For a case study of downhole microseismic monitoring, it is determined that the new expressions provide station-dependent arrival-time uncertainties, which are used as weights to improve source location uncertainties.

Downhole Microseismic Monitoring Using Time-Division Multiplexed Fiber-Optic Accelerometer Array

Microseismic monitoring is of importance for several geoscience research aspects and for applications in oil and gas industry. For signals generated by the ultra-weak microseismic events, conventional moving-coil geophone systems have reached their limit in detection sensitivity especially at high frequency range. Here we for the first time present a specially tailored fiber-optic sensing system targeting at downhole microseismic monitoring. The system contains 30 individual interferometric accelerometers and 2 reference sensors, which are time-division multiplexed into a 12-level vector seismic sensor array. The multiplexed accelerometers can achieve ~50 ng/ $\sqrt {\mathrm{Hz}} $ noise equivalent acceleration, which is superior to the commercial available moving-coil geophone systems at frequencies above 200 Hz. The measured sensitivity of the accelerometers can reach ~200 rad/g from 10 Hz to 1 kHz. The dynamic range is above 134 dB over the same frequency range and is higher than its electronic counterpart in the low frequency band. Moreover, the sensors can function properly under the harsh condition of 120 °C temperature and 40 MPa pressure over the 4-hour test duration. The sensor array along with the interrogator has been running uninterruptedly over 3 weeks in a multi-stage hydraulic fracturing stimulation field test. On-site results show that our system can clearly resolve the vector nature of both compressional and shear waves generated by the microseismic events.

Approximate traveltime inversion in downhole microseismic monitoring

ABSTRACTIn downhole microseismic monitoring, accurate event location relies on the accuracy of the velocity model. The model can be estimated along with event locations. Anisotropic models are important to get accurate event locations. Taking anisotropy into account makes it possible to use additional data – two S‐wave arrivals generated due to shear‐wave splitting. However, anisotropic ray tracing requires iterative procedures for computing group velocities, which may become unstable around caustics. As a result, anisotropic kinematic inversion may become time consuming. In this paper, we explore the idea of using simplified ray tracing to locate events and estimate medium parameters. In the simplified ray‐tracing algorithm, the group velocity is assumed to be equal to phase velocity in both magnitude and direction. This assumption makes the ray‐tracing algorithm five times faster compared to ray tracing based on exact equations. We present a set of tests showing that given perforation‐shot data, one can use inversion based on simplified ray‐tracing even for moderate‐to‐strong anisotropic models. When there are no perforation shots, event‐location errors may become too large for moderately anisotropic media.

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.