Porosity Prediction from Offshore Seismic Data of F3 Block, the Netherlands using Multi-Layer Feed-Forward Neural Network

Kushwaha, Prabodh Kumar; Maurya, S. P.; Rai, Piyush; Singh, N. P.

doi:10.18520/cs/v119/i10/1652-1662

Cited by 14 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The correlation between the well-side channel and the well-acoustic transit time curve retrieved by the neural network reaches 92%. This study also compared multilayer feedforward neural networks (Lu and Jin, 1999; Dai et al, 2014; Kushwaha et al, 2020). The correlation between the well bypass and the well curve of the latter is 76%.…”

Section: Resultsmentioning

confidence: 99%

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

Guo

Yang

Guo

et al. 2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

In order to accurately predict reservoirs with similar physical properties between sandstone and surrounding rocks, this study takes Carboniferous-Permian system in southeastern Ordos Basin as an example, and puts forward a multi-attribute probabilistic neural network prediction method based on acoustic time difference compaction correction. Firstly, the time-frequency analysis method is used to divide the frequency of the original acoustic time difference curve, and the low-frequency long-trend correction of all wells is carried out based on the low-frequency components of standard wells. Then, it is frequency-divided and fused with the original high-frequency components to form new acoustic time difference data, and the long-term trend decompaction correction of acoustic time difference is completed, so that the overlapping area of acoustic time difference between reservoir and surrounding rock is reduced. Multi-attribute probabilistic neural network is an algorithm based on sampling points, before reservoir prediction, inversion parameters must be optimized to save calculation time and reduce prediction error. There is a high correlation between longitudinal velocity and lithologic change trend on the inversion profile of sound velocity. The comparison results of various inversions show that the velocity profile of multi-attribute probabilistic neural network has the highest coincidence rate and high resolution with the velocity curve of logging acoustic wave. The example shows that this method has high accuracy in predicting reservoir thickness, especially for the strata with little difference in physical properties between reservoir and surrounding rock.

show abstract

Section: Resultsmentioning

confidence: 99%

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

Guo

Yang

Guo

et al. 2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…PNN's mechanism matches human behavior [46][47][48][49]. Another form of neural network is the PNN [11][12][13]50]. It depends on the estimation of the probabilistic density function of Parzen.…”

Section: Geostatistical Techniquesmentioning

confidence: 99%

“…In the present study, four types of seismic inversion namely, model-based inversion (MBI), colored inversion, bandlimited inversion, and maximum likelihood sparse spike inversion methods are used to estimate subsurface acoustic impedance. The MBI is a form of post-stack inversion that estimates acoustic impedance (P-impedance) from seismic data with inputs from well logs [10][11][12][13]. In this method, the error between synthetic and seismic data is minimized by updating the model and the least error model gives final results.…”

Section: Introductionmentioning

confidence: 99%

“…BLI method is the most common form of the inversion method, which presumes that the seismic amplitude is related to the coefficient of reflection and transforms the input seismic trace into P-impedance traces. The input seismic trace is normally wavelet processed [12]. Further, the MLSSI technique is the next inversion method applied here, but unlike other techniques, it provides a reflectivity series calculation that would estimate the seismic information with a minimum amount of (Sparse) spikes.…”

Section: Introductionmentioning

confidence: 99%

“…In this scenario, non-uniqueness is expected to be taken care of by implementing the sparse criterion of reflectivity. To achieve this, the maximum likelihood deconvolution (MLD) was performed [10,12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Petrophysical Properties using Post Stack Seismic Inversion and Geostatistical Techniques over F-3 Block, Netherlands - A Comparative Study

Kushwaha,

Singh,

Maurya

et al. 2023

Int. J. Petrol. Technol.

Self Cite

View full text Add to dashboard Cite

To estimate petrophysical parameters, seismic inversion techniques have been frequently used for estimating attributes like P-impedance, elastic impedance, S-impedance, density, Vp / Vs ratio, and gamma-ray logs from seismic and well-log data. These characteristics enable us to comprehend subsurface lithology for geo-seismic analysis, including its extent and shape. Four different post-stack inversion techniques, including bandlimited inversion (BLI), colored inversion (CI), maximum likelihood sparse spike inversion (MLSSI), and model-based inversion (MBI), have been applied to the post-stack seismic data from the F3 block in the Netherlands in this study. The objective is to compare the efficacy of these inversion methods over F3 block seismic data. For post-stack inversion, the data was inverted into a very high-resolution P-impedance volume. The analysis depicts that all four inversion methods provide mutually consistent subsurface information with marginally better MBI results. Furthermore, geostatistical techniques have been used intensively for further testifying the results obtained from post-stack inversion methods. The geostatistical techniques use seismic data-derived attributes and inverted impedance-derived attributes as input to estimate P-wave velocity, porosity, and density away from the boreholes. Two geostatistical methods namely probabilistic neural network (PNN) and multilayer feed-forward neural network (MLFN) are used for the analysis. Porosity, density, and P-wave velocity have been predicted using both techniques which highlighted different characteristics of the subsurface with very detailed information. The derived results show that reservoir properties have been better estimated with the combination of MBI and PNN techniques for F3 block data over the other methods used in this study.

show abstract

Estimation of Petrophysical Properties Using Linear Programming Sparse Spike Inversion and Deep Feed-Forward Neural Network Techniques Over F3 Block, Netherlands: A Case Study

Singh,

Kushwaha,

Maurya

et al. 2024

Pure Appl. Geophys.

View full text Add to dashboard Cite

Porosity Prediction from Offshore Seismic Data of F3 Block, the Netherlands using Multi-Layer Feed-Forward Neural Network

Cited by 14 publications

References 0 publications

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

Prediction of Petrophysical Properties using Post Stack Seismic Inversion and Geostatistical Techniques over F-3 Block, Netherlands - A Comparative Study

Estimation of Petrophysical Properties Using Linear Programming Sparse Spike Inversion and Deep Feed-Forward Neural Network Techniques Over F3 Block, Netherlands: A Case Study

Contact Info

Product

Resources

About