Canning, Anat (Paradigm) | Moulière-Reiser, Dominique (Paradigm) | Weiss, Yuval (Paradigm) | Malkin, Alex (Paradigm) | Phillip, Eitan (Paradigm) | Grinberg, Nimrod (Paradigm) | Teitel, Anastasya (Paradigm) | Reznikov, Margaret (Paradigm) | Yehezkel, Vardit (Paradigm)
Summary We present an artificial intelligence approach for enhancing the frequency spectrum of seismic data. We used synthetic seismic data as a training dataset for constructing a neural networks operator that can solve the posed problem. We then applied this operator to real seismic data and obtained broader frequency spectrum. Introduction Artificial intelligence algorithms have received a lots of attention in recent years. In the context of seismic data processing and interpretation, the range of problems which can benefit from an artificial intelligence approach is very broad.
Interpolation between known wavelets is essential in many seismic processing and inversion workflows. We have found that interpolation results are highly dependent on the specific input wavelets. We have also found that a straightforward method does not always provide the required answer. Although this is not a new problem, it seems that it has not attracted a great deal of attention, and alternative methods for trivial interpolation have not been explored in the literature. In this paper we define several interpolation methods that can deal with various cases that we have encountered.
Presentation Date: Tuesday, October 18, 2016
Start Time: 3:45:00 PM
Presentation Type: ORAL
The azimuth information derived from prestack seismic data at target layers is very sensitive to many aspects of the analysis process, making reliability a serious issue. This is true for both AVAZ and VVAZ analysis. In this paper we analyze some of the major factors affecting the reliability of the results and show our approach to dealing with the main obstacles.
Azimuthal seismic analysis is becoming increasingly important, due to the growing interest in unconventional shale plays, where stress direction and facture orientation are the most sought-after information. The challenge is to extract maximum information about the nature of the azimuthal variations from wide-azimuth, migrated, prestack data. In this paper we refer to azimuthal variations which can be characterized by three parameters (α1, α2, ß) and (G1,G2,ß). α1 and α2 are the primary axis of a velocity ellipse, G1 and G2 are the primary axis of an AVAZ gradient ellipse, and ß is the orientation angle (Grechka and Tsvankinm, 1998; Ruger, 1998). The source of these azimuthal variations can be HTI, TTI or orthorhombic anisotropy. Obviously, there are specific layers, typically carbonates or shales, which exhibit such azimuthal variations and are of particular interest. The common practice is to generate a horizon oriented map for the target layers displaying attributes of interest, e.g. orientation angle ß, (α1 - α2), similar to the Eagle Ford example displayed in Figure 1.
The workflow for extracting azimuthal information along target layers normally involves picking the geometry of the horizon and displaying one or more of the above azimuthal anisotropy parameters as the color value of the horizon map. This produces horizon maps such as the one displayed in Figure 1. The main problem with this method is reliability. Like many geoscientists who perform these workflows, we noticed that the picture map obtained from such an analysis is very sensitive to workflow and parameter changes, and one can easily obtain very different pictures from the same data. We are used to methodologies in which establishing the quality and robustness of the result (e.g. velocity analysis or migrations), is relatively straightforward. However, analyzing attributes along a horizon oriented map does not provide clear insight that can help users establish the reliability of their data. This statement is generally true for every “horizon oriented attribute map”, but in our case, the lack of such clear insight starts with the data itself, even before it is extracted onto a horizon slice. This becomes the main obstacle in the analysis of azimuthal information and is “dangerous” because it may lead to the interpreter depending on unreliable data.
A workflow for analyzing azimuthal anisotropy of target layers (shale, carbonates, etc.) from migrated seismic gathers is presented. This workflow involves a very effective automated algorithm for RMO analysis for the three parameters that characterize azimuthal anisotropy. The antistrophic zones are automatically identified and analyzed. This is a very robust methodology which provides reliable anisotropic attribute maps along target horizons.