High Resolution Processing Methods For Acoustic Log

With the gradual development of exploration and exploitation for oil and gas, it is pressing to solve the problem of distinguishing thin bed, reevaluating old well logs and seeking neglected oil beds. Therefore, well log must be high resolution processed so as to enhance the accuracy of evaluating thin bed.Multipole array acoustic logging has been applied widely by oil fields at present. Existing array waveforms processing techniques pursue accuracy of each wave component's slowness calculated, while neglecting its vertical resolution. In addition, since it is difficult to build model, the applied effect of existing high resolution processing methods for conventional sonic log is not completely satisfactory though it has acquired some effects.Many methods of wave component's pick-up and high resolution processing for array acoustic log have been investigated fully in this paper. Based on comparison of these methods' merits and demerits, the processing method of correlation cross power spectrum combined with multiple-shot is presented aiming at multipole array acoustic logging. It processes the shortest common-source subarrays and common-receiver subarrays(0.152m) to extract acoustic slowness with cross power spectrum method. Then all the slowness of the same depth interval formation spanned by all the shortest common-source subarrays and common-receiver subarrays is averaged to ensure the accuracy and reliability of the slowness computed while obtaining high vertical resolution slowness curve. Apply the technique to array acoustic logging data in Daqing Oil Field, process eight well's data of array acoustic logging, and compare its results with the results through conventional method and the measured high resolution slowness log to analyze its applied effects. This shows that the method is able to enhance slowness' vertical resolution reliably and effectively, so it has very high applied value.Based on analyzing the vertical response characteristic of conventional sonic logging, researching thoroughly the very complex nonlinear relationship between conventional sonic log with low resolution and slowness log with high resolution, forecast model is established using training samples consisting of key wells' slowness curves with high, low vertical resolution to create the other well's slowness curve with higher vertical resolution by means of Artificial Neural Networks(ANN) technique. Actual data have been processed selecting some eight wells in Daqing Oil Field as object of establishing model and forecasting. It is feasible to apply ANN technique to high resolution processing for conventional sonic log theoretically and has acquired some effects practically. Hence, it has wide applied perspective as a new attempt in high resolution processing for conventional logs.