A matching method for integrating multiscale components to model elastic parameters. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Han, Feilong; Zhang, Hongbing; Guo, Qiang; Shang, Zuoping; Li, Tiancai
• Is Part Of: Exploration geophysics. Volume 50:Issue 5(2019)
• Page Start: 532
• Page End: 540
• Publication Date: 2019-09-03
• Subjects: Modelling -- elastic -- petrophysics -- log analysis -- attributes; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1080/08123985.2019.1634687
• Languages: English
• ISSNs: 0812-3985

Abstract

ABSTRACT: Seismic inversion and reservoir identification are complicated problems with integrated data sets and need a velocity or density model to ensure the stability of the results. However, an elastic parameter model, such as velocity or density, is difficult to obtain with high precision by conventional migration or interpolation, especially during the early exploration stage, which contains few available logging data. The kriging method has proved to be an effective technique for mineral exploration and petroleum geophysics, and has led to a series of expansive techniques in earth science. In this paper, we proposed a new matching method, base value compensation (BVC), for integrating multiscale data sets into model elastic parameters. Considering the variety of original information, we used multiple constrain cokriging for three types of data set at different scales. These three data sets come from logging, seismic attributes and sedimentary facies information. The P-wave velocity model is stable and more representative of the subsurface condition, but we found that this method produces few points with anomalous values in the density model. Those abnormal points cause substantial loss of geological detail at sedimentary boundaries. In most modelling methods, this shortcoming is due to the fact that these three input parameters have significantly different properties. The proposed method can effectively solve this problem by matching the input data sets at a similar observational scale before modelling. We demonstrated this method using a case study in the South China Sea. The decrease in abnormal points in the final modelling results verifies the effectiveness of BVC.