Experimental Research of Compound Monitoring on Multiple Temporary Blocking Refracturing for Long-Section Horizontal Wells

As an important energy replacement block in China, the tight conglomerate oilfields in the Mahu area are difficult to develop and are characterized by strong heterogeneity, large horizontal stress differences, and undeveloped natural fractures. However, new development processes including temporary blocking diversion and large section-multiple clusters have been implemented on the oilfields in the past few years. In 2020, two adjacent horizontal wells in the MD well area experienced a poor fracturing development effect compared with the earlier wells in this area. Analysis suggests that the main reasons are water sensitivity of the reservoir, insufficient fracturing scale, and/or interference from the adjacent old wells. To ameliorate the problem, this study presents an experimental study of multiple temporary plugging and refracturing technology in long horizontal well sections, in combination with electromagnetic and microseismic monitoring. Results from the study show a great difference between the two monitoring techniques, which is attributed to their different detection principles. Interestingly, the combination of the two approaches provides a greater performance than either approach alone. As the fracturing fluid flow diversion is based on temporary plugging diversion and electromagnetic monitoring of fracturing fluid is advantageous in temporary plugging diversion monitoring, both approaches require further research and development to address complex situations such as multiple temporary plugging and refracturing in long intervals of adjacent older wells.

SOURCE LOCATION MECHANISM OF MICROSEISMIC MONITORING USING P-S WAVES AND ITS EFFECT ANALYSIS

Seismic source location is the most fundamental and most important problem in microseismic monitoring. However, only P wave has been mostly applied in the existing microseismic monitoring networks, with low location accuracy and poor stability of location result for the microseismic events occurring beyond monitoring networks. The seismic source location was implemented using P wave and S wave in this study to expand the effective monitoring area of a microseismic monitoring network and improve its location accuracy for microseismic events nearby the monitoring network. Then, the seismic source location mechanism using P-S wave was revealed through theoretical derivation and analysis. Subsequently, the program development and numerical simulation were combined to analyze and compare systematically the location effects of differently distributed monitoring networks, those consisting of different quantities of sensors, and those with S wave contained in some sensors under two circumstances: combination of P wave and S wave and single use of P wave. Results demonstrate that adding S wave in the plane enhances the accuracy control in the radius direction of the monitoring network. After S wave is included, the location accuracy within a certain area beyond the monitoring network is improved considerably, the effective monitoring area of the whole network is expanded, and the unstable location zones using only P wave are eliminated. The location results of differently distributed monitoring networks and the influence laws of the quantity of sensors constituting the networks on the location results are acquired. This study provides evidence for microseismic monitoring to realize accurate and stable location within a larger range. Keywords: seismic source location, P wave and S wave, mechanism, location effect

Stability Analysis of Surrounding Rock in Multi-Discontinuous Hydraulic Tunnel Based on Microseismic Monitoring

The diversion tunnel of a hydropower station is characterized by low quality surrounding rock and weak structural planes. During excavation, rock mass spalling and cracking frequently occur. To evaluate the stability of a rock mass during tunnel excavation, high-precision microseismic monitoring technology was introduced to carry out real-time monitoring. Based on the temporal and spatial distribution characteristics of microseismic events, the main damage areas and their influencing factors of tunnel rock mass were studied. By analyzing the source characteristic parameters of the concentration area of microseismic activities, the rock fracture mechanism of the concentration area was revealed. The 3D numerical model of diversion tunnel was established, and the deformation characteristics of the rock mass under the control of different combination types of weak structural planes were obtained. The results showed that the microseismic event was active between 29 October 2020 and 6 November 2020, and the energy release increased sharply. The main damage areas of the rock mass were located at Stakes K0 + 500–K0 + 600 m. Microseismic source parameters revealed that shear failure or fault-slip failure induced by geological structures had an important influence on the stability of the surrounding rock. The numerical simulation results were consistent with the microseismic monitoring results and indicated that among the three kinds of structural plane combination types, including “upright triangle”, “inverted triangle” and “nearly parallel”, the “upright triangle” structure had the most significant influence on the stability of the surrounding rock. In addition, the maximum displacement of the surrounding rock had a trend of lateral migration to the larger dip angle in the three combined structural plane types. The research results will provide significant references for the safety evaluation and construction design of similar tunnels.

Study on the Formation Mechanism of Rock Burst Caused by Seam Floor Slip under an Ultrathick Conglomerate

A rock burst accident occurred on coalface 13230 of the Gengcun Coal Mine in Henan Province. Through a field investigation, theoretical analysis, and microseismic monitoring, we studied how the rock burst, which was caused by overall seam floor slip and instability, occurring under an ultrathick conglomerate. Because the overlying ultrathick conglomerate in the mined-out zone close to coalface 13230 had been inadequately mined, the leading section of the coalface was under a high level of stress. Combined with the tectonic stresses from the F16 faultage and the soft floor structure, these factors caused the floor of this coalface to trigger the overall slip-type rock burst. In this paper, an estimation model of the ultimate bearing capacity of a seam floor under an ultrathick conglomerate and the advancing abutment pressure on the coalface is presented. This model is used to show that the ultimate bearing capacity of the seam floor on coalface 13230 is 26.3 MPa, and the abutment pressure is far more than the floor bearing capacity. We also present pressure relief and reinforced supporting measures, which can effectively prevent floor slip-type rock bursts from occurring. The results of this study provide a reference for the prevention and control of floor slip-type rock bursts in coal mining under an ultrathick conglomerate.

Seismic-while-drilling by drill-bit source and large-aperture ocean-bottom array

Seismic while drilling (SWD) by drill-bit source has been successfully used in the past decades and is proven using variable configurations in onshore applications. The method creates a reverse vertical seismic profile (RVSP) dataset from surface sensors deployed as arrays in the proximity of the monitored wells. The typical application makes use of rig-pilot reference (pilot) sensors at the top of the drill-string and also downhole. This approach provides while-drilling checkshots as well as multioffset RVSP for 2-D and 3-D imaging around the well and prediction ahead of the bit. For logistical (sensor deployment) and cost (rig time related to technical installation) reasons the conventional drill-bit SWD application is typically much easier onshore than offshore. We present a novel approach that uses a network of passive-monitoring sea bottom nodes pre-deployed for microseismic monitoring to simultaneously and effectively record offshore SWD data. We study the results of a pilot test where we passively monitored the drilling of an appraisal well at the Wisting discovery in the Barents Sea with an ocean-bottom cable deployed temporarily around the drilling rig. The continuous passive recording of vibration signals emitted during the drilling of the well provides the SWD data set, which is treated as a reverse vertical seismic profile. The study is performed without rig-pilot signal. The results are compared with legacy data and demonstrate the effectiveness of the approach and point to future applications for real-time monitoring of the drilling progress, both in terms of geosteering the drill bit and predicting formation properties ahead of the bit by reflection imaging.

Single Well Microseismic Monitoring Leveraging Hybrid Cable Combining Both DAS and Traditional Geophones

The single monitoring well configuration is a favorable option for microseismic monitoring considering risk and cost. It has commonly been used in various industries for decades. When using a single monitoring well, we rely among other things on the waveforms’ polarization information to accurately locate detected microseismic events. Additionally, using a large array aperture reduces hypocenter's uncertainty. Instead of solely relying on 3C geophones to achieve such objectives, we propose to combine 3C sensors and distributed acoustic sensing (DAS) equipment. It is quite a cost-effective solution, and it enables us to leverage each system's strength while minimizing their respective limitations when considered individually. We present the technical feasibility of such a hybrid microseismic monitoring system using data acquired during a monitoring campaign performed in the Montney formation, Canada. In this dataset, the optic fiber (DAS) is located in the wireline cable used to deploy the 3C geophones; themselves located at the bottom of the DAS wireline cable. Though different acquisition systems are employed for the geophone array and the DAS array, both datasets are GPS time stamped so that data can be processed properly. We scan the DAS data using an STA/LTA event detection, and we integrate with the 3C geophone data. We find the microseismic waveform in both the DAS and the geophone sections and confirm the arrival times are consistent between DAS and geophones. Once datasets are merged, we determine hypocenters using a migration-based event location method for such hybrid array. The uncertainty associated with the event located using the hybrid DAS – geophone array is smaller than for any of the systems looked at independently thanks to the increased array aperture. This case study demonstrates the viability and efficiency of the next generation of a single well acquisition system for microseismic monitoring. Not only does it lower event location uncertainty, but it is also more reliable and cost-effective than the conventional approaches.

GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.