UNCONVENTIONAL SEISMIC INTERPRETATION WORKFLOW TO ENHANCE SEISMIC ATTRIBUTES

INTRODUCTION

Seismic interpretation techniques have been developed very fast in last twenty years because of increasing amount of seismic data and development in hardware, acquisition, and processing. Seismic interpretation algorithms give a lot of techniques to help interpreter for extraction valuable information from seismic data (Chopra and Marfurt(2012)), so interpreter needs to understand seismic data and determine his target to choice the best techniques suitable for his case study. Fig.1 is a time line that shows at a glance the developments that took place in seismic interpretation from 1956 to 2008 (Liner, 2008).

When using traditional methods, it is often difficult to get a clear and unbiased view of faults and stratigraphic features hidden in the 3-D data. Faults are (often) readily seen on individual vertical cross-sections, but many of these must be examined to determine the lateral extent of faulting. Stratigraphic changes are difficult to detect on vertical seismic lines because of the limited profile they present in this view. Time slices are more suitable for detecting and following faults and stratigraphy laterally ((Bahorich and Farmer (1995)).

After 43 years of attribute development, it should not be surprising that many of these attributes are redundant, and some are even useless (Barnes, 2007). Seismic attributes can enable interpreter to understand seismic data very well and generate new view for his model, but there are hundreds of seismic attributes divided into many classes that make interpreters afraid of using new things. Subsequent developments by Taner et al. (1979) of instantaneous attributes generated initial excitement, but seismic attributes didn’t come into common usage until the advent of 3D interpretation workstations when Bahorich and van Bemmel (1994) showed that one could make maps of these attributes along interpreter-generated surfaces.

    Murfart (Marfart, 2014) divides the future of attribute development into five categories – feature recognition, prestack attribute development, multiattribute cluster analysis, enhanced interpreter-computer interaction, and the statistical correlation of attributes to completion techniques and reservoir production.

Fig. 1: Timeline showing developments in seismic interpretation Modified from Liner (2008).

It is important to identify the interested geobodies from seismic data but unfortunately the conventional seismic interpretation cannot extract a lot of information from seismic data so modern seismic interpretation needs to extract all available geological information in less time and high accuracy. The only way to think in new way is to look at data by new eyes and use all geophysical information that can extract from seismic data using unconventional seismic interpretation.

In this paper, we design a simple workflow that we hope that help seismic interpreters to define petroleum geophysical prospecting and also help to extract geological features from seismic data by both conventional and unconventional ways as shown in Fig. 2.

Seismic interpretation workflow has been developed for data preconditions and quality control tests for seismic data by spectral analysis, band pass filter and mean smooth filter using unconventional interpretations to enhance post stack seismic data and compare results of similarity before and after applying data preconditions for geobody extraction.

                                           Fig. 2: Workflow for seismic interpretation.

METHODOLOGY

It is hard to determine the best workflow for geophysical study so we need to merge experience with latest technology to solve complex geological problems and enhance results of seismic interpretation. In this paper, it is a trial to combine both conventional and unconventional seismic interpretations to extract all available geological information from seismic data as shown in Fig. 2 that represents seismic interpretations workflow that enables interpreter to reduce risk and get results very fast by mixing experience interpretation with latest technology.

It is important to reduce noise effect and increase the signal to noise ratio, to enhance seismic data started by quality control to study how noise effect on seismic data and generate spectrum analysis to determine amount of noise and seismic bandwidth frequencies. Then, apply band pass filter to remove high frequency and low frequency noises and mean smooth filter to overcome random noises that affecting on the interested seismic data band frequencies. After data preconditions applied similarity attributes have been used to extract geobodies by available autopicking algorithms.

Seismic conventional and unconventional exploration workshop Diploma

1-      Seismic interpretation in different applications 2H

·         Compare between (kingdom, opendetect, petrel,….)

2-      Data loading  4 hours

·         Seg Y viewer

·         Seg Y editing

·         2D and 3d seismic Data loading  (kingdom , petrel, opendetect)

·         Loading well information (location , TD , FT , Log ,..)

·         Advanced loading for Horizons and faults countoures , grids for different applications (kingdom, petrel , opendatect,..)

·         Editing file for data loading

3-      QC seismic Data in different applications 4H

·         Filtering for structure interpretations

·         Processing workflow for stratigraphic

·         Check workflow

·         Filter with edge preserve and without edge preserve

·         Generation preconditioning workflow using different software

4-      Well seismic tie 4H

·         Check shot

·         Sonic  

·         Integration  sonic and check shot

·         Generation synthetic in different application

5-      Conventional seismic interpretation 2d 4H

·         Mistie analysis

·         Horizon picking

·         Fault picking

6-      Conventional seismic interpretation 3d 4H

·         Different Horizon auto picking algorithm

·         Fault interpretation (manual, semi manual , auto)

·         3d visualization for seismic interpretations

7-      Depth conversion and velocity modeling for seismic interpretation 6H

·         Different between signal layer and multi layer depth conversations

·         Depth conversion (interval velocity, average velocity , stacking velocity)

·         How to generate different workflow for depth conversion

·         High definition  velocity  modeling

8-      Seismic image interpretations 4H

·         Determination best seismic attributes for different geological targets

·         Seismic attributes parameters (conditioning)

·         Visualization seismic attributes  for identifying geobodies

9-      Unconventional seismic attributes  interpretations 4H

·         Hybrid attributes

o   Crossplote attributes

o   Mathematic hybrid attributes

o   Visualization multiattributes

o   Neural net work

o   PCA principles component analysis

o   Pdf properties Distribution Function

10-  Spectral decomposition SD 2H

o   Different types for SD

o   Determination tuning thickness and Dominant frequencies

o   Different Visualization method

o   Real Average SD attributes

11-  Avo and inversion 4H

o   Prestake data loading

o   Avo analysis

o   Seismic colored inversion

o   Based model inversion

o   Neural network inversion

Seismic Attributes for Lithological Change

There are many seismic attributes that can be used for direct hydrocarbon indicators (DHI) and lithological changes. Some attributes depend on prestack data like AVO attributes, others depend on poststack data like sweetness and Q attenuation.
 

Poststack attributes can help to predict the presence of hydrocarbon and lithology change along different geobodies. Unfortunately in our case study we do not have wells information across the interested channels, but poststack attributes help for the isolation of geobodies from other not interested geological features using direct hydrocarbon indictors like average energy attribute that help in determination of the best location for prospect wells along channels
 

Most of the attributes discussed previously are defined as "Primitive" attributes. They basically measure a particular physical or geometrical condition or statistics. In spite of they are primitive; they can also be used as building blocks by combining them logically. They can be used to detect more complicated preconditions. The logic of the combination is experience and knowledge based. For example, the fluid factor computed by the AVO analysis is a hybrid attribute based on experience

Since their introduction they have been shown to be effective discriminators. There will continue in developing additional hybrid attributes as we incorporate an additional knowledge of rock physics, geology and reservoir characteristics.
It is very important to find good relation between two or three attributes that help for less risk and can help to generate new attributes. In order to combine attributes we must determine what relation between attributes, usually the use physical attributes with each other can help to follow change in lithology or we can use relation for DHI, in this case study we used both envelope and sweetness attributes and try to find good relation that can use for DHI along geobody

 

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