Facies Classification Using Stochastic Inversion Method for Carbonate in Pangkah, North East Java Basin, Indonesia

2018 ◽  
Author(s):  
D. Kusuma
Keyword(s):  
Inversion Method ◽  
Stochastic Inversion ◽  
North East ◽  
Facies Classification

scholarly journals Gas Saturated Sandstone Reservoir Modeling Using Bayesian Stochastic Seismic Inversion

2020 ◽  
Vol 5 (1) ◽  
pp. 25-31
Author(s):  
Rahmat Catur Wibowo ◽  
Ditha Arlinsky Ar ◽  
Suci Ariska ◽  
Muhammad Budisatya Wiranatanagara ◽  
Pradityo Riyadi
Keyword(s):  
Scale Up ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reservoir Modeling ◽  
Inversion Method ◽  
Tight Sandstone ◽  
Stochastic Inversion ◽  
A Value ◽  
Sandstone Reservoir ◽  
Stochastic Seismic Inversion

This study has been done to map the distribution of gas saturated sandstone reservoir by using stochastic seismic inversion in the “X” field, Bonaparte basin. Bayesian stochastic inversion seismic method is an inversion method that utilizes the principle of geostatistics so that later it will get a better subsurface picture with high resolution. The stages in conducting this stochastic inversion technique are as follows, (i) sensitivity analysis, (ii) well to seismic tie, (iii) picking horizon, (iv) picking fault, (v) fault modeling, (vi) pillar gridding, ( vii) making time structure maps, (viii) scale up well logs, (ix) trend modeling, (x) variogram analysis, (xi) stochastic seismic inversion (SSI). In the process of well to seismic tie, statistical wavelets are used because they can produce good correlation values. Then, the stochastic seismic inversion results show that the reservoir in the study area is a reservoir with tight sandstone lithology which has a low porosity value and a value of High acoustic impedance ranging from 30,000 to 40,000 ft /s*g/cc.

scholarly journals Calculation of signal spectrum by means of stochastic inversion

2010 ◽  
Vol 28 (7) ◽  
pp. 1409-1418 ◽  
Author(s):  
T. Nygrén ◽  
Th. Ulich
Keyword(s):  
Fourier Transform ◽  
Sampling Rate ◽  
Digital Signal ◽  
Close Relation ◽  
Signal Spectrum ◽  
Inversion Method ◽  
Stochastic Inversion ◽  
Data Points ◽  
Sinusoidal Functions ◽  
The Fourier Transform

Abstract. The standard method of calculating the spectrum of a digital signal is based on the Fourier transform, which gives the amplitude and phase spectra at a set of equidistant frequencies from signal samples taken at equal intervals. In this paper a different method based on stochastic inversion is introduced. It does not imply a fixed sampling rate, and therefore it is useful in analysing geophysical signals which may be unequally sampled or may have missing data points. This could not be done by means of Fourier transform without preliminary interpolation. Another feature of the inversion method is that it allows unequal frequency steps in the spectrum, although this property is not needed in practice. The method has a close relation to methods based on least-squares fitting of sinusoidal functions to the signal. However, the number of frequency bins is not limited by the number of signal samples. In Fourier transform this can be achieved by means of additional zero-valued samples, but no such extra samples are used in this method. Finally, if the standard deviation of the samples is known, the method is also able to give error limits to the spectrum. This helps in recognising signal peaks in noisy spectra.

Stochastic inversion of prestack seismic data using fractal-based initial models

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. R47-R59 ◽  
Author(s):  
R. P. Srivastava ◽  
M. K. Sen
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Fractal Theory ◽  
Well Logs ◽  
Inversion Method ◽  
Frequency Variation ◽  
Well Log ◽  
Statistical Parameters ◽  
Stochastic Inversion ◽  
Frequency Variations

In general, inversion algorithms rely on good starting models to produce realistic earth models. A new method, based on a fractional Gaussian distribution derived from the statistical parameters of available well logs to generate realistic initial models, uses fractal theory to generate these models. When such fractal-based initial models estimate P- and S-impedance profiles in a prestack stochastic inversion of seismic angle gathers, very fast simulated annealing — a global optimization method — finds the minimum of an objective function that minimizes data misfit and honors the statistics derived from well logs. The new stochastic inversion method addresses frequencies missing because of band limitation of the wavelet; it combines the low- and high-frequency variation from well logs with seismic data. This method has been implemented successfully using real prestack seismic data, and results have been compared with deterministic inversion. Models derived by a deterministic inversion are devoid of high-frequency variations in the well log; however, models derived by stochastic inversion reveal high-frequency variations that are consistent with seismic and well-log data.

scholarly journals Application of a stochastic inversion method for estimating the sound speed in the ocean bottom

1995 ◽  
Vol 98 (5) ◽  
pp. 2972-2972
Author(s):  
Kazuhiko Ohta ◽  
Itaru Morishita ◽  
Shunji Ozaki ◽  
Tsuneo Ishiwata
Keyword(s):  
Sound Speed ◽  
Inversion Method ◽  
Ocean Bottom ◽  
Stochastic Inversion

A Stochastic Inversion Method Based On Multi-point Geostatistics

2017 ◽  
Author(s):  
X.Y. Liu ◽  
X.H. Chen ◽  
J.Y. Li ◽  
K.K. Guo ◽  
L. Zhou ◽  
...  
Keyword(s):  
Inversion Method ◽  
Stochastic Inversion

scholarly journals Seismic characterization of a carbonate reservoir in Tarim Basin

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. B177-B188 ◽  
Author(s):  
Yan Liu ◽  
Yanghua Wang
Keyword(s):  
Tarim Basin ◽  
Carbonate Reservoir ◽  
Seismic Facies ◽  
Western China ◽  
Stochastic Inversion ◽  
Facies Classification ◽  
Impedance Inversion ◽  
Seismic Characterization ◽  
Lithofacies Classification

Seismic characterization of carbonate reservoirs is a challenging task for geophysicists because of their special depositional environment and complex interior structures. We developed a case study of the seismic characterization of a karstified carbonate reservoir in the Tarim Basin, western China. The characterization procedure is sequential and includes fault and fracture detection, seismic facies classification, seismic impedance inversion, and lithofacies classification. We presented a dip-steered coherence algorithm for detecting faults and karst fractures in the carbonate reservoir. Incorporating the dip information improves the performance and robustness. We applied normalized seismic segments, rather than the amplitude values, as the input to seismic facies classification, so as to reduce the impact of strong amplitudes, such as karst fractures, and to enable the analysis of weak amplitudes in the background strata. For the impedance inversion, we adopted a Fourier integral method for fast simulation in the stochastic inversion in this karstified carbonate reservoir. The algorithm honors the lateral variation based on the seismic trace similarity, instead of the lateral variogram that is commonly used in stochastic inversion. We conducted lithofacies classification, in which we used seismic coherence as a prior knowledge, so as to honor the fracture-associated local lithofacies with dolomitization and to distinguish it from limestone without dolomitization. Based on reservoir characterization described above, we determined three drilling wells for potential oil/gas exploration.

An improved stochastic inversion method and its application in Bohai Bay

2019 ◽  
Author(s):  
Xinyi Duan ◽  
Huihuang Tan ◽  
Jun Guo ◽  
Yao Li
Keyword(s):  
Inversion Method ◽  
Bohai Bay ◽  
Stochastic Inversion
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