Spectral-Velocity Prediction of Geological Section Types and Reservoir Properties (SVP-Technology)

Mikhail Afanasyev

The method of complex spectral-velocity prediction of geological section types and reservoir properties was developed on the basis of spectral-time analysis (STAN) [1].

The physical basis of SVP-technology is that, in accordance with the classical theory of propagation of elastic vibrations, changes in the elastic properties of the medium under the influence of variability of lithofacies and granulometric characteristics of rocks, reservoir parameters and the presence of fluid, causing changes in shape of the impulse wave and its propagation velocity.

The most complete display of the changes of shape of the pulse is achieved by its two-dimensional spectral decomposition of the axes of frequency and time (STAN).

Spectral-time analysis of seismic records (SWAN) is a filtered seismic traces, using a sequence of filters. For this purpose we use two-octaves, zero phase bandpass filters with a triangular frequency characteristic with a variable width. The bandwidth of these filters increases with increasing frequency.

The result of STAN transformations of seismic record is a set of traces, which is called STAN-column.

STAN-analysis differs from other methods of spectral decomposition the fact that it studies the spectrum of the STAN-column, not the spectrum of the seismic signal. The main advantage of STAN-analysis is that he is studying the dependence of the amplitude of the frequency and time and is resistant to noises. This allows us to obtain reliable results.

STAN-column has the frequency and time energy spectra. These energy spectra are characterized by six quantitative spectral-time attributes (STA). Attributes of the frequency axis: STA1 - the ratio of the energy of high frequencies to the energy of low frequencies, STA2 - the product of the specific spectral density to the weighted average frequency, STA3 - the product of the specific spectral density to the maximum frequency.

Attributes of the time axis: STA4 - the ratio of the energy of large times to the energy of small times, STA5 - the product of the specific spectral density to the weighted average time, STA6 - the product of the specific spectral density to the maximum time. Changes of the velocity of propagation of seismic waves in a heterogeneous environment is determined by the values of pseudoacoustic velocity. Thus, quantitative spectral-time and pseudoacoustic characterization of seismic record gives seven relevant attributes - six spectral-time (on the frequency axis and on the time axis) and one pseudoacoustic (velocity). Spectral-time attributes, calculated in three dimensions interwell space, called the volumetric spectral-time attributes VSTA.

These seven seismic attributes are certified by the maximum value of the cross-correlation coefficient with the types of geological section and reservoir properties at wells. Then we calculate selected attributes for all seismic traces. In this way we create maps and cubes of optimal attributes that are most closely related to the reservoir properties at wells. Sets of maps and cubes are interpreted using advanced mathematical tools - artificial neural networks and statistical, spectral and correlation algorithms.

The results of SVP-Technology represent the cubes and maps of geological section types, porosity and permeability coefficients, effective thickness, specific capacity and hydraulic conductivity of productive horizons.

The experience of use in many different areas and fields of oil and gas provinces shows that this prediction method gives good results in carbonate and clastic sediments, for with a porous, fracture, and fracture-cavitary structure.

SVP-Technology is based on new methods of geophysical prospecting, which are protected by nine patents for the invention of the Russian Federation SVP-Technology is based on new methods of geophysical prospecting, which are protected by nine patents for the invention of the Russian Federation and recommended for the use of "Guidelines on the use of seismic data (2D, 3D) in the calculation of oil and gas reserves", Ministry of Natural Resources, the State Commission for Reserves of the Russian Federation 2006, page 23.

However, with all the benefits, SVP-Technology allows you to build a thick-layer model. Minimum interval of researches of SVP-Technology is 25-30 milliseconds. On most of deposits of the Russian Federation thickness of reservoirs changes from 0.5-1 to 15-20 m. Therefore, the current method can’t achieve the required accuracy of prediction, which, despite the study of thin layer, is made in considerably larger interval (25-30 ms at a velocity 2500 m/s corresponds to 31-38 m). To solve this problem is used the law of Walter and Golovkinsky, whereby changes in the thin layer correspond to changes in the surrounding thicker layer.

Now the author develops a new approach that can increase the resolution and the detail of the results, different from the previous one, instead of estimated pseudoacoustic velocity uses cubes and maps of pseudoacoustic impedance obtained as a result of stochastic inversion.

The novelty of the proposed approach is as follows:

As one of the attributes used cubes and sections of impedance obtained as a result of stochastic inversion. Stochastic inversion allows to select the most probable model, using simulation annealing.

Cubes and maps of impedance complex interpretation with cubes and cards spectral-time attributes that are closely related to the reservoir properties in the physical meaning. It means that we use physically diverse attributes.

Such combining provides more reliable thin-layer model of the distribution of reservoir properties and gives the opportunity to study thin productive layers, without the assumption that follows from the law of Walter and Golovkinsky.

Thus, we create a thin-layer model, in which the minimum thickness of the studied layers, equal to 5-10 m. The author theoretically proved the validity of using such a set of attributes.

Petrophysical model based on this new method doesn’t show the weighted average porosity in thirty-millisecond interval but the distribution of reservoir properties in real thin layers and not of. Calculation of reserves based on this petrophysical model will be much more accurate.

References

Kopilevich E. A, Mushin I. A, Davidova Е. А, Afanasyev M. L. Complex spectral-velocity forecasting of types of a geological section and reservoir porosity and permeability properties. Moscow-Izhevsk Institute of Computer Research, Scientific and Engineering Center “Regular and Chaotic Dynamics”, 2010, 248 p.

AAPG Search and Discovery Article #90163©2013AAPG 2013 Annual Convention and Exhibition, Pittsburgh, Pennsylvania, May 19-22, 2013