Gas Saturated Sandstone Reservoir Modeling Using Bayesian 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.

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Prediction of Diagenetic Facies Using Well Logs in Tight Sandstone Reservoir - A Case from Chang 7 in Ordos Basin, China

78th EAGE Conference and Exhibition 2016 ◽

10.3997/2214-4609.201600704 ◽

2016 ◽

Author(s):

Y. Cui ◽

G. Wang ◽

Y. Sun ◽

Y. Ran ◽

Z. Zhou

Keyword(s):

Ordos Basin ◽

Well Logs ◽

Tight Sandstone ◽

Sandstone Reservoir ◽

Diagenetic Facies

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Deterministic Seismic Inversion in a Tight Sandstone Reservoir and Integration in the Geological Model of the Irharen Gas Field (Timimoun Basin, SW Algeria)

10.3997/2214-4609.2019x60047083 ◽

2019 ◽

Author(s):

G. Rodrigo ◽

P. Alonso ◽

GY. Brenner ◽

R. Faro ◽

L. Serra

Keyword(s):

Seismic Inversion ◽

Geological Model ◽

Tight Sandstone ◽

Gas Field ◽

Sandstone Reservoir

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An Analysis on Isochronal Facies-Controlled Pre-Stack Seismic Inversion Prediction of Tight Sandstone Reservoir- A Case of Fuyu Formation in Zhaoyuan Area in North Songliao Basin, China

10.2523/iptc-19130-ms ◽

2019 ◽

Author(s):

Meiyi Chen ◽

Qingsheng Ji ◽

Shoutian Chen ◽

Longpu Qin ◽

Peihong Cong

Keyword(s):

Seismic Inversion ◽

Songliao Basin ◽

Tight Sandstone ◽

Sandstone Reservoir

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Comparing the Odisi Integrated Stochastic Seismic Inversion Method to an Equivalent Bayesian Inversion Method

79th EAGE Conference and Exhibition 2017 ◽

10.3997/2214-4609.201700622 ◽

2017 ◽

Author(s):

M. Walker ◽

S. Grant ◽

H. Pham Huu ◽

Y. Zheng

Keyword(s):

Seismic Inversion ◽

Bayesian Inversion ◽

Inversion Method ◽

Stochastic Seismic Inversion

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Facies-based seismic inversion for a field development plan

The Leading Edge ◽

10.1190/tle38100770.1 ◽

2019 ◽

Vol 38 (10) ◽

pp. 770-779

Author(s):

Ehsan Zabihi Naeini ◽

Jalil Nasseri

Keyword(s):

Seismic Inversion ◽

Thin Layers ◽

Reservoir Modeling ◽

Inversion Method ◽

Technical Solution ◽

Hydrocarbon Production ◽

Field Development ◽

Development Plans ◽

Central North Sea ◽

Probabilistic Nature

Field appraisal and development plans aim to provide the best technical solution for optimizing hydrocarbon production and require integration between various disciplines including geology, geophysics, engineering, well planning, and environmental sciences. Seismic inversion could provide one essential component for reservoir modeling in support of appraisal and development evaluations. Therefore, it is important to quantitatively assess all of the possibilities and uncertainties involved in reservoir definition and extension. A probabilistic facies-based seismic inversion method has been utilized to achieve this goal in a recent Central North Sea discovery. The probabilistic nature of the inversion allows computation of various scenarios. We categorically selected, among others, most likely, optimistic, and pessimistic scenarios based on prior knowledge and calibration at the wells. Then, we performed a statistical analysis of all of the scenarios to identify the uncertainties. We also performed a postinversion forward-modeling study to assess uncertainties that may be related to thin layers of subseismic resolution.

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

Geophysics ◽

10.1190/1.3379322 ◽

2010 ◽

Vol 75 (3) ◽

pp. R47-R59 ◽

Cited By ~ 34

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.

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