Improved First-Motion Algorithm to Compute High-Resolution Sonic Log

Abstract

Abstract Estimation of formation slowness is of significant value for petrophysical and geomechanical applications. To do this, a source excites a signal in the borehole, which couples with, and propagates within, the formation prior to being recorded by receivers. Generally, sonic tools are composed of one or more transmitters and an array of receivers. A next generation sonic tool developed by Schlumberger is composed of 3 monopole and 2 dipole transmitters. The receiver array is composed of 13 receivers separated by 6 inches. Waveforms are digitized at the sensor with a dynamic range of 16 bits. Digitizing the waveform at the sensor will guarantee optimal data quality as the waveforms will not be corrupted by electronic noise or other undesirable effects. The general technique used to measure formation slowness is based on semblance processing which was first proposed 20 years ago. Semblance processing is robust and provides reliable results in most cases but its vertical resolution is linked to the length of the tool array. Practically, it means it is not possible to detect beds that are smaller than a tool's array length. Hence, it can be difficult to compare slowness logs with other datasets of much higher vertical resolutions. In order to alleviate this problem, we propose an improved first motion algorithm to compute a high-resolution sonic log. The technique considers that the part of the waveform before and after the first break can be modeled as an AR process. The key parameters of this approach are the "model order", which describes the data and the part of the waveform to be modeled. In this paper we will show that the use of the Bayesian Information Criterion combined with the computation of the envelope of the signal will allow for an automatic computation that is suitable for wellsite operations. We will demonstrate that this first motion technique does not require any human intervention and in most cases provides a robust and reliable result. This algorithm will then be applied on real data and the high-resolution log obtained will be compared with other high vertical resolution measurements. Introduction Evaluation of formation slowness using sonic tools is important in oil exploration. This physical measurement allows geophysicists to calibrate their velocity models, and petrophysicists to obtain a porosity estimate of the reservoir. Measurement of the compressional and shear acoustic slownesses of the formation is usually made in open hole. The conventional method is to acquire monopole data, which is used to determine the compressional slowness. If the shear slowness is required, and if it is faster than the mud slowness, it can be obtained from monopole data with waveform processing. In slow formations dipole mode data may also be acquired to provide shear information and can provide redundancy for the monopole shear measurement in fast formations. These results are generally well established and reliable in most cases.