Time Attributes of Stratigraphic Surfaces, Analyzed in the Structural and Wheeler Transformed Domain

Bruin, G. de; Bouanga, Eric

doi:10.3997/2214-4609.201401624

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Exploring the oil and gas reserves in the Upper-Jurassic to Lower Cretaceous layer, which is often located below the stated interval, was the goal of this survey. Reflectors from the Miocene, Pliocene, and Pleistocene make up the upper part up to 1200ms [15,44,45]. With a very high porosity, sand and shale make up the deltaic bundle (20-42%).…”

Section: Study Areamentioning

confidence: 99%

“…In this method, the error between synthetic and seismic data is minimized by updating the model and the least error model gives final results. On the other hand, the CI method is another post-stack inversion method in which the inversion is interpreted as a convolutional procedure where a frequency domain operator is utilized to convert the seismic traces into acoustic impedance [14,15]. The bandlimited inversion methods use the relation between impedance and reflectivity for the inversion of seismic data.…”

Section: Introductionmentioning

confidence: 99%

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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.

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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.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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.

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show abstract

“…This survey was carried out for exploring oil and gas in the layer of Upper-Jurassic to Lower Cretaceous generally found below the certain interval. The upper part up to 1200 ms is made up of reflectors belonging to the Miocene, Pliocene, and Pleistocene (De Bruin and Bouanga 2007;Wolak et al 2013). The deltaic bundle is comprised of sand and shale, which has very high porosity (20-42%).…”

Section: The Study Areamentioning

confidence: 99%

Use of maximum likelihood sparse spike inversion and probabilistic neural network for reservoir characterization: a study from F-3 block, the Netherlands

Kushwaha

Maurya

Singh

et al. 2019

J Petrol Explor Prod Technol

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Maximum likelihood sparse spike inversion (MLSSI) method is commonly used in the seismic industry to estimate petrophysical parameters in inter-well region. In present study, maximum likelihood sparse spike inversion technique is applied to the processed 3D post-stack seismic data from the F-3 block, the Netherlands, for estimation of acoustic impedance in the region between the wells. The analysis shows that the impedance varies from 2500 to 6200 m/s/*g/cc in the region which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and original seismic trace is found to be 0.93 and the synthetic relative error as 0.369, which indicate good performance of the algorithm. The analysis also shows low-impedance anomaly in between 600 and 700 ms time interval which may be due to the presence of sand formation. Thereafter, the probabilistic neural network analysis is performed to predict porosity along with multi-attribute transform analysis to estimate P-wave velocity and porosity in inter-well region. These parameters strengthen the seismic data interpretation which is very crucial step of any exploration and production project. The method is first applied to the composite traces near to well locations, and results are compared with well log data. After getting reasonable results, the whole seismic section is inverted for the P-wave velocity and porosity volume. The analysis shows anomaly in between 600 and 700 ms time interval which corroborates well with the low-impedance zone which may correspond to the reservoir. This is preliminarily interpretation; however to confirm a reservoir, there is need for more petrophysical parameters to be studied. Keywords Maximum likelihood sparse spike inversion (MLSSI) • Multi-attribute linear regression • Probabilistic neural network (PNN) Abbreviations MLSSI Maximum likelihood sparse spike inversion PNN Probabilistic neural network HRS Hampson Russell software MLD Maximum likelihood deconvolution CC Correlation coefficient RMS Root mean square MLFN Multilayer feed forward neural network BLI Band-limited inversion CI Colored inversion LPSSI Linear programming sparse spike inversion MBI Model-based inversion RL Reinforcement learning

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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.

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Time Attributes of Stratigraphic Surfaces, Analyzed in the Structural and Wheeler Transformed Domain

Cited by 9 publications

References 4 publications

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

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

Use of maximum likelihood sparse spike inversion and probabilistic neural network for reservoir characterization: a study from F-3 block, the Netherlands

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

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