Study On Array Acoustic Logging Tool And Its Data Processing Method

Array acoustic logging tool is an indispensable logging tool during the production process of oil logging. The domestic developed array acoustic logging tools lag behind the foreign advanced ones in technology such as information acquisition accuracy, information acquisition reliability, logging data processing and logging interpretation. Developping modern array acoustic logging tool and the correspending data processing and interpretation software with our own intellectual property is an urgent and important research project. Combining with some array acoustic tool research task during doctoral study and focusing on the tool's technical realization in this dissertation, the author research on the following three aspects: the design of downhole signal acquisition and processing system, the logging data processing, the logging information interpretation and engineering application. The main works and contributions of the dissertation are as follows:(1) The research on the architecture design and the implementation of the tool's downhole signal acquisition and processing system.Based on analysis of the operation principle of array acoustic logging tool and the characteristic of acoustic logging signal, the downhole signal acquisition and processing system is designed as three modules: the controller module, the signal acquisition and processing module and the signal conditional module.At the same time, the tool's performance of anti-interference, reliability and low-power dissipation are taken full account into the design.Through experiments and improvement in the design, the system can run reliably in the downhole working environment of high temperature and high pressure.(2) The research on Logging data processing method.Logging data processing is an important work of logging interpretation and it is the quality assurance of logging interpretation accuracy. In downhole real-time data processing, because of the raw logging data are noised by downhole high temperature, high pressure and high vibration working environment, the data must be preprocessed to remove the interference.So dowhole real-time digital filter and automatic gain controlling processing method are researched. At the same time, two quality control parameters, the downhole head wave arrival time and the downhole time difference, are calculated to enhance logging process monitor. In the surface field data processing, an algorithm based on energy ratio of short-window and long-window is presented to improve the detection accuracy of surface head wave arrival time. Then a new method which is based on cross-correlation coefficient with interpolation is presented to improve the accuracy of calculations for surface time difference. In the surface post-period data processing, a new method for high-resolution processing of well logs based on anti-aliasing Shannon wavelet packet transform algorithm is proposed to enhance the explanation of thin beds.(3) The research on the method of down hole real-time data compression and high-speed data transmission. For traditional cable logging system, a downhole real-time data compression method based on improved SPIHT algorithm which can be used in the downhole tool is presented for decreasing data transmission content. Nextly, a logging cable high-speed data transmission method based on ADSL technology is presented for raising data transmission rate. For LWD (Logging While Drilling) system, with the transmission rate is very low, and a lot of logging data must be stored in downhole tool, a data compression algorithm based on wavelet neural network is presented in the dissertation. Experimental results show that the algorithm can improve compression ratio and save data storage space greatly. Next, the characteristics of mud pulse transmission channel of LWD system are analyzed in detail. Then a mud signal detection method based on wavelet neural network is discussed. Experimental results show that the signal can be detected from strong noise and the goal of data telemetry in LWD system can be realized by the means of the method.(4) The research on the intelligent interpretation method of logging reservoir stratum.Firstly, Lyapunov stability theory was used to discuss the convergence conditions of single particle. Then, based on that, a new strategy was introduced to improve the performance of the PSO (Particle Swarm Optimization) algorithm. Secondly, the improved PSO algorithm is used to train the parameters of the WNN (Wavelet Neural Network) instead of the gradient algorithm. Then a high efficient classifier based on improve PSO-WNN is created. Finally, the new classifier is used to for intelligent interpretation of logging reservoir stratum.