scholarly journals Use of Electrofacies, Spectral Decomposition and Neural Network seismic inversion technique to analyze a fluvial channel system, Browse Basin, Western Australia

2019 ◽  
Author(s):  
Diego Perez ◽  
Lilik Hardanto
Keyword(s):  
Neural Network ◽  
Western Australia ◽  
Spectral Decomposition ◽  
Seismic Inversion ◽  
Channel System ◽  
Inversion Technique

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Post-Stacking Acoustic Seismic Inversion Using Recurrent Neural Network

2009 ◽  
Author(s):  
Caio Graco Pereira Santos ◽  
Alexandre Gonçalves Evsukoff ◽  
Webe João Mansur
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Seismic Inversion

Characterization of fluvio-deltaic gas reservoirs through AVA deterministic, stochastic, and wave-equation-based seismic inversion: A case study from the Carnarvon Basin, Western Australia

2020 ◽  
Vol 39 (2) ◽  
pp. 92-101
Author(s):  
Arturo Contreras ◽  
Andre Gerhardt ◽  
Paul Spaans ◽  
Matthew Docherty
Keyword(s):  
Wave Equation ◽  
Elastic Properties ◽  
Western Australia ◽  
Mode Conversion ◽  
Seismic Inversion ◽  
Gas Reservoirs ◽  
Amplitude Variation ◽  
Avo Inversion ◽  
Amplitude Data ◽  
Carnarvon Basin

Multiple state-of-the-art inversion methods have been implemented to integrate 3D seismic amplitude data, well logs, geologic information, and spatial variability to produce models of the subsurface. Amplitude variation with angle (AVA) deterministic, stochastic, and wave-equation-based amplitude variation with offset (WEB-AVO) inversion algorithms are used to describe Intra-Triassic Mungaroo gas reservoirs located in the Carnarvon Basin, Western Australia. The interpretation of inverted elastic properties in terms of lithology- and fluid-sensitive attributes from AVA deterministic inversion provides quantitative information about the geomorphology of fluvio-deltaic sediments as well as the delineation of gas reservoirs. AVA stochastic inversion delivers higher resolution realizations than those obtained from standard deterministic methods and allows for uncertainty analysis. Additionally, the cosimulation of petrophysical parameters from elastic properties provides precise 3D models of reservoir properties, such as volume of shale and water saturation, which can be used as part of the static model building process. Internal multiple scattering, transmission effects, and mode conversion (considered as noise in conventional linear inversion) become useful signals in WEB-AVO inversion. WEB-AVO compressibility shows increased sensitivity to residual/live gas discrimination compared to fluid-sensitive attributes obtained with conventional inversions.

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Lithogeomorphological facies analysis of Upper Miocene coal-prone fluviodeltaic reservoirs, Northern Malay Basin

2019 ◽  
Vol 7 (3) ◽  
pp. T565-T579 ◽  
Author(s):  
Ismailalwali A. M. Babikir ◽  
Ahmed M. A. Salim ◽  
Deva P. Ghosh
Keyword(s):  
Spectral Decomposition ◽  
Velocity Ratio ◽  
Shear Velocity ◽  
Seismic Inversion ◽  
Delta Plain ◽  
Coal Beds ◽  
Open Sea ◽  
Daunting Task ◽  
Flow Directions ◽  
Prestack Seismic Inversion

The Group E stratigraphic unit is a significant gas producer in the Northern Malay Basin. However, due to the thinly bedded nature of the sandstone reservoirs, thick shale, and abundant coal beds, accurate seismic attributes interpretation of lithology and fluid prediction has been a daunting task. To address this problem, we have conducted an integrated seismic sedimentology workflow using spectral decomposition, color blending, waveform classification, prestack seismic inversion, and stratal slicing to characterize the lithogeomorphological facies of the coal-bearing reservoirs. On spectral decomposition and waveform classification maps, we clearly identified depositional elements such as the distributary channel, distributary mouth bar, subaqueous levee, and interdistributary fill. We computed the elastic properties through prestack seismic inversion to obtain good lithology discrimination between coal and gas-charged sandstone. Both lithologies are characterized by low acoustic impedance, but the compressional to shear velocity ratio ([Formula: see text]) of coal is high compared to gas-charged sandstone. The current interpretation indicated that the Group E interval was deposited in a delta plain setting. The varying flow directions of the distributary channels in the area support the hypothesis that describes the Malay Basin during Miocene time as a narrow gulf, connected to an open sea to the south and flanked by deltas and fan deltas.

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