Study on the Source Characteristics of Downhole Airgun with Different Excitation Environments

The measurement of underground medium variation using a repeated artificial source has gradually become an important goal to pursue. In recent years,we have developed and improved a technology system with large capacity airguns excited in land reservoirs by transplanting marine seismic exploration technology. The excitation effect has a close relationship to airgun capacity,water environment,and excitation conditions. In view that large capacity airgun must be excited without a water environment,we expand the system to use in downhole. Based on the BHS-2200 LL downhole airgun with a capacity of250in3,this paper carries out a comparative analysis on the characteristics of an airgun source excited in 0. 2m- and 5. 0m-diameter wells,and the results show that:( 1) The dominant frequency of the airgun signal excited in a 5. 0m well is mainly from 10 Hz to40Hz,lower than that in a 0. 2m well,and the larger body of water is good for bubble oscillation.( 2) In terms of exciting energy,the signal excited in a 5. 0m well has stronger energy than in a 0. 2m well,with a difference of 1 order in magnitude,and the signal can be detected up to 9km excited in a 5. 0m well with a single shot.( 3) The airgun signal has good repeatability in both excitation wells. The downhole airgun excitation technology system has potential application in dynamic monitoring near a fault zone with a small scale range, exploration of oil and mineral resources, and modern urban geophysical environment.

Modal Parameter Identification Method of Jacket Platform Structure Based on AFDD and Optimized FBFFT

Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.