Qingming Xie

Due to the complicated interference sources and low signal-to-noise ratio of seismic records, conventional seismic inversion methods are difficult to accurately identify shale gas reservoirs with a thickness of less than 10 m. This presents great challenges to shale gas exploration and development in China. Seismic phase-controlled nonlinear stochastic inversion (SPCNSI) is related to the heterogeneity of underground media. With the constraints of the stratigraphic sequence or seismic facies models, the minimum value between the seismic model and seismic record can be solved through iterative processes. Based on the solved acoustic velocity in formation, the constraints for SPCNSI can be formed with the matching relationship between target layers and different sequences in three-dimensional space. The prediction resolution of an unconventional reservoir can be effectively improved by combining logging, geological and seismic information. The method is suitable for predicting thin shale gas reservoirs in complex geological structures. In this study, SPCNSI was developed to predict the thin-layer marine shale gas reservoir in the southeast of Chongqing; the horizontal and vertical distribution characteristics were proven in the Longmaxi Formation and the Wufeng Formation; thin layers with a thickness of less than 7 m were also discovered. According to the results, three sets of potential development areas were determined by the inversion method, and the accuracy and reliability of the method were verified by logging and productivity testing.

A large amount of data would be generated during the process of microseismic monitoring, and data transmission with the cable has some shortcomings such as poor anti-interference, time-consuming, laborious, high cost, and low construction efficiency. On the other hand, the influence of signal diffraction and the multipath effect will greatly cut down the energy of the signal during the wireless transmission in the complex mountainous area. The wireless transmission system based on Wi-Fi is designed in this paper, which includes the base station and data transceiver. The maximum distance and transmission rate at point-to-point communication of the base station are up to 1000 m and 150 Mbps, respectively, and it provides the communication backbone line for the network. The data transceiver adopts the network protocol based on Ad Hoc, and the transmission distance is up to 200 m at the condition of complex topography and lush mountains. We have applied the system to in-site microseismic monitoring with 28 geophones of the coalbed methane fracturing in South Chongqing. It achieved a good performance with a transmission rate of 1.2 Mbps, a time delay of 1.95 ms, and a signal strength of up to −52.3 dBm for real-time data transmission in the field. The results show that the system has the advantages of low BER, fast transmission speed, long communication distance, and stable and safety hardware and has a great value for more applications of microseismicity in complex topography.

With monitoring of the acoustic emission phenomenon caused by rock deformation and failure, microseismic monitoring has been widely used in the development of unconventional oil and gas fields. Due to the complex environment and diversity types of the noise, the signal energy of surface microseismic monitoring is weak and the signal-to-noise ratio (S/N) of raw data is very low. In the process of data processing, many human resources are needed to discriminate the first-break picking because of the low S/N, and this directly affects the error of microseismic event location. We have adopted the regularization to Stein unbiased risk estimation (R-SURE) algorithm based on the continuous wavelet transform to separate the signal from the noise in different decomposition levels. The regularization factor is the adaptive change of the different geology and fracturing engineering, which is related to shale brittleness, fracturing pressure, and displacement. As a result, the threshold from the R-SURE algorithm is multiresolution in different levels, and the S/N could be improved effectively. In addition, we established the threshold discriminant for picking up the first-break wave of low-S/N data combined with the Akaike information criterion and characteristic function, which compared the maximum absolute value in the time window. The method has good robustness and low computational complexity. The first arrival is automatically and accurately judged, which improves the accuracy of the event location. We successfully applied these methods to the surface microseismic monitoring of shale gas fracturing in several wells in southwest China. The S/N of the raw data has been improved, the effective stimulated reservoir volume and the performance of the gas production are predicted with the results, which provides important technical support for shale gas development in the area.