APPLICATION OF HADAMARD TRANSFORM FOR RESERVOIR LITHOFACIES DISCRIMINATION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

EBRAHIMI MOHAMMAD REZA; RIAHI MOHAMMAD ALI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF GEOPHYSICS Year:2012 | Volume:6 | Issue:4 Page(s): 1-11

Download Full-Text

Persian Verion

View:

707

Download:

Cites:

Information Journal Paper

Title

APPLICATION OF HADAMARD TRANSFORM FOR RESERVOIR LITHOFACIES DISCRIMINATION

Author(s)

EBRAHIMI MOHAMMAD REZA | RIAHI MOHAMMAD ALI | Issue Writer Certificate

Keywords

HADAMARD TRANSFORMQ2

WALSH FUNCTIONQ2

RESERVOIR FACIESQ2

POROSITYQ2

SARVAK FORMATIONQ2

NEURAL NETWORKQ3

Abstract

This study applies the translation invariant attribute (TIA) using the HADAMARD TRANSFORM of the seismic data to discriminate lithofacies. The HADAMARD TRANSFORM (also known as the Walsh–HADAMARD TRANSFORM, Hadamard–Rademacher–Walsh transform, Walsh transform, or Walsh–Fourier transform) is an example of a generalized class of Fourier transforms. It performs an orthogonal, symmetric operation on 2nreal numbers (or complex numbers, although the Hadamard matrices themselves are purely real). The HADAMARD TRANSFORM can be regarded as being built out of size-2 Discrete Fourier Transforms (DFTs), and is in fact equivalent to a multidimensional DFT of a size. It decomposes an arbitrary input vector into a superposition of 2*2*2...*2 WALSH FUNCTIONs.In mathematical analysis, the set of WALSH FUNCTIONs form an orthogonal basis of the square functions on the unit interval. The functions take the values -1 and+1 only, on sub-intervals defined by dyadic fractions. The orthogonal WALSH FUNCTIONs are used to perform the HADAMARD TRANSFORM, which is very similar to the way the orthogonal sinusoids are used to perform the Fourier transform. The WALSH FUNCTIONs are related to the Rademacher functions; They both form a complete orthogonal system.The HADAMARD TRANSFORM is particularly good at finding repeating, stacked vertical sequences. The dyadic shifts represent the invariant properties of the HADAMARD TRANSFORMs. The output of a translation invariant transform is insensitive to the dyadic shifts so that in geologic applications, the objective of using these transforms is to find a geologic pattern which have been analyzed anywhere in the time series, irrespective of their vertical position.If z is the output of a dyadic shift invariant transform, such as the HADAMARD TRANSFORM, of a sequence x, then the dyadic shift invariant power spectrum ( Sz2), is termed as the translation invariant attribute. The translation invariant attribute computation requires 2n input samples. If an input sequence does not have 2n samples, then either zero padding or quite a large time window can be used to make 2n samples.This attribute is applied in 3D seismic data of SARVAK FORMATION of one of the oil fields in the south-west of Iran. The SARVAK FORMATION for this oilfield is a carbonate unit gradually overlying the Kazhdumi Formation. The thickness of SARVAK FORMATION increases towards the west and varies between 582 m and about 700 m. The RESERVOIR FACIES for this field are classified based on their porosities. Four POROSITY facies were selected by using POROSITY logs of four vertical wells drilled in this oil field. All the seismic data are converted to those categories by Artificial NEURAL NETWORK (ANN). The NEURAL NETWORK used here was a Two-layer Feed-forward network with Error Back Propagation (EBP) for learning algorithms. The transfer function of the hidden neurons was hyperbolic tangent and the transfer function of the output neurons was linear. Three different time slices of HADAMARD TRANSFORM, translation invariant attribute were presented. The correlation between the real POROSITY and the predicted POROSITY using ANN was estimated to be about 81%. Finally, all the seismic data were converted to POROSITY facies by using ANN and three time slices of the POROSITY facies were calculated and shown.

Cites

No record.

References

No record.

Cite

APA: Copy

EBRAHIMI, MOHAMMAD REZA, & RIAHI, MOHAMMAD ALI. (2012). APPLICATION OF HADAMARD TRANSFORM FOR RESERVOIR LITHOFACIES DISCRIMINATION. IRANIAN JOURNAL OF GEOPHYSICS, 6(4), 1-11. SID. https://sid.ir/paper/133761/en

Vancouver: Copy

EBRAHIMI MOHAMMAD REZA, RIAHI MOHAMMAD ALI. APPLICATION OF HADAMARD TRANSFORM FOR RESERVOIR LITHOFACIES DISCRIMINATION. IRANIAN JOURNAL OF GEOPHYSICS[Internet]. 2012;6(4):1-11. Available from: https://sid.ir/paper/133761/en

IEEE: Copy

MOHAMMAD REZA EBRAHIMI, and MOHAMMAD ALI RIAHI, “APPLICATION OF HADAMARD TRANSFORM FOR RESERVOIR LITHOFACIES DISCRIMINATION,” IRANIAN JOURNAL OF GEOPHYSICS, vol. 6, no. 4, pp. 1–11, 2012, [Online]. Available: https://sid.ir/paper/133761/en

Related Journal Papers