Research On Distributed Fiber Optic Vibration Signal Logging Data Processing Method And Production Prediction

Distributed fiber optic vibration signal logging is a technology that uses fiber optic to sense the vibration signals returned from different formations or well walls to analyze the surrounding formation characteristics or downhole events,which has the advantages of strong real-time monitoring results and high reliability of interpretation results.However,distributed fiber optic vibration signal as a four-dimensional information carrier cannot fully describe the physical characteristics of the signal from both time and frequency dimensions,and also has the defects of large data volume which is difficult to store and process,and the signal is sensitive to interference.At the same time,a mature method for calculating production from distributed fiber optic vibration signal logging and related log processing and interpretation software has not been developed in oil and gas exploration and production.Therefore,it is crucial to conduct research on the data processing methods and software development of distributed fiber optic vibration signal logging.Based on the monitoring principle of distributed fiber optic vibration sensing system,this paper establishes a data processing process for the analysis of spectrogram practicality after data preprocessing,and then calculates the yield by dividing the fiber optic data frequency band method,and the process is integrated into data processing interpretation software using C# programming technology.In the data pre-processing,the fast Fourier transform and short-time Fourier transform methods are first used to perform time-frequency analysis of the vibration signal,which more comprehensively depicts the signal characteristics.For the large data volume of fiber optic vibration signals,the Douglas-Pook algorithm is used for data extraction and thinning to retain its signal characteristics while simplifying the data.Since the sensitivity of distributed fiber optic vibration signals makes them highly responsive to external vibrations,distributed fiber optic vibration signals often contain interference from multiple similar information components,and thus require denoising of vibration signals.In this paper,an ARO-VMDSSA denoising method is creatively proposed,i.e.,the penalty factor and the number of decomposition layers in the Variational Mode Decomposition(VMD)algorithm are optimized by using Artificial rabbit optimization(ARO).The final denoised signal is obtained by reconstructing the decomposed subsignal using Singular Spectrum Analysis(SSA).The experimental results show that this algorithm has obvious advantages over wavelet analysis filtering,Empirical Mode Decomposition(EMD)filtering,Variational Mode Decomposition(VMD)filtering in terms of spectral map,root mean square error,signal-to-noise ratio,etc.The VMD filtering can maintain a good filtering effect while preserving the low-frequency local features of the signal.The analysis of the spectrograms after the data pre-processing operation shows that the distributed fiber optic vibration signal logging has significant advantages in wellbore flow monitoring,wellbore integrity analysis,and hydraulic fracturing monitoring,while revealing the problem that the spectrograms cannot visually determine the downhole events and determine the production.After that,the K-means++ algorithm is proposed to divide the vibration signal frequency bands to represent different downhole events,and the amplitude mean curve envelope area of the reservoir-related frequency bands is used to calculate the relative production of each production formation.The experimental results correspond well with the relative absorption data interpreted from conventional production logs,and the yield interpretation is highly accurate.Finally,this paper integrates the whole set of data processing processes into an independent program software,which fills the vacancy of the method and software in the field of distributed fiber optic vibration signal logging in China and strongly promotes the development of intelligent construction of oil and gas fields.