scholarly journals Application of the Acoustic Impedance (AI) Seismic Inversion and Multi-Attribute Method for Reservoir Characterization in Bonaparte Basin

2021 ◽  
Vol 4 (1) ◽  
pp. 8-20
Author(s):  
Maurin Puspitasari ◽  
Ambran Hartono ◽  
Egie Wijaksono ◽  
Tati Zera
Keyword(s):  
Acoustic Impedance ◽  
Stepwise Regression ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Impedance Method ◽  
Linear Regression Method ◽  
Seismic Method ◽  
Impedance Inversion ◽  
Southwest Part ◽  
Acoustic Impedance Method

Research on the application of the acoustic impedance (AI) seismic inversion and multi-attribute method was conducted with the aim to characterize the reservoir in the Bonaparte Basin. The modeling which used in the acoustic impedance inversion seismic method is model-based. Meanwhile, the multi-attribute seismic method used log porosity that appliying the linear regression method and using the stepwise regression technique. Based on the result of the sensitivity analysis and analysis using the seismic inversion acoustic impedance method, the sandstone reservoir zone that has the prospect of hydrocarbons containing gas is located in the Northeast-Southwest part of the study area which in WCB-1, WCB-3 and WCB-4 well with the acoustic impedance values are in the range of 4,800 - 13,000 (m / s) * (g / cc), and the porosity values generated from the analysis using the multi-attribute seismic method are in the range of 5 - 16% in WCB-1 and WCB-4, 2 - 10% on WCB-3.

Rock Property Cross-Plot Analysis and Post-Stack Acoustic Impedance Inversion for Optimal Reservoir Characterization in Aplha Field, Onshore Niger Delta Basin

2021 ◽  
Author(s):  
George-Best Azuoko ◽  
Amobi Ekwe ◽  
Amulu Emmanuel ◽  
Ayatu Usman ◽  
Eluwa Ndidiamaka ◽  
...  
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Seismic Inversion ◽  
Hydrocarbon Reservoirs ◽  
Event Time ◽  
Plot Analysis ◽  
Impedance Inversion ◽  
Acoustic Impedance Inversion ◽  
Niger Delta Basin

Abstract In the quest to recover by-passed hydrocarbons, extend the life of mature fields, increase hydrocarbon reserves and satiate the increasing global demand for energy, the need for robust reservoir characterization using acoustic impedance inversion continues to grow. In this study, petrophysical parameters were evaluated for two sand intervals RX2 and RX5. Detailed cross-plot analysis of robust petrophysical properties, (density, water Saturation, Lambda-rho and Mu-rho and Porosity) facilitated fluid and lithology discrimination. Well to seismic correlations and acoustic-Impedance model-based, 3-D seismic inversion was done using Hampson Russell software, while petrophysical attribute slices and event-time structure maps were extracted at two horizons - H1 and H2. Results show that RX2 is 100ft thick in Well A, ranging from 5860ft to 5960ft, and 141ft thick in Well B, ranging from 5794ft to 5935ft. Interval RX5, 71ft thick, ranges from 6447ft to 6518ft in Well A, and 88ft thick in Well B, ranging from 6447ft to 6535ft. These intervals had average densities of 2.20g/cc for RX2 and 2.23g/cc for RX5 in Well A. In well B, density values are 1.95g/cc in RX2 and 2.06g/cc for RX5. Average porosities of 25.5% and 27.5% in RX2 and RX5 respectively for Well A; 29% and 19% in RX2 and RX5 respectively for Well B were observed. Respectively, average water and hydrocarbon saturation values of 0.31Swand 0.69Shfor Well A; 0.51Swand 0.49Shfor Well B, was recorded in both intervals. From the results, the thicknesses of RX2 and RX5 conform to the standard thickness of hydrocarbon reservoirs in the study area. Furthermore, the discrimination of the reservoir contents into fluid and lithology by the cross plots, and the observations in the attribute slices indicate that the selected intervals RX2 and RX5 are viable conventional hydrocarbon reservoirs.

scholarly journals Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

2020 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Shiba Rezaei ◽  
Ahmed Mohamed Ahmed Salim ◽  
Sayed Hesammoddin Kazemeini ◽  
Deva Prasad Ghosh
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Well Location ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Lithofacies Classification ◽  
Elastic Inversion ◽  
Significant Match

Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

scholarly journals Seismic Interpretation and Inversion Leading to an Accurate Reservoir Characterization in a Carbonate Environment

2021 ◽  
Vol 2 (12) ◽  
pp. 1229-1230
Author(s):  
Yasir Bashir ◽  
Nordiana Mohd Muztaza ◽  
Nur Azwin Ismail ◽  
Ismail Ahmad Abir ◽  
Andy Anderson Bery ◽  
...  
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Seismic Interpretation ◽  
And Inversion

Seismic data acquired in the field show the subsurface reflectors or horizon among the geological strata, while the seismic inversion converts this reflector information into the acoustic impedance section which shows the layer properties based on lithology. The research aims to predict the porosity to identify the reservoir which is in between the tight layer. So, the output of the seismic inversion is much more batter than the seismic as it is closer to reality such as geology. Seismic inversion is frequently used to determine rock physics properties, for example, acoustic impedance and porosity.

JOINT IMPEDANCE INVERSION AND SPECTRAL DECOMPOSITION FOR DEEPWATER GAS RESERVOIR CHARACTERIZATION: A CASE STUDY IN SOUTH CHINA SEA

2020 ◽  
pp. 1-74
Author(s):  
Yaneng Luo ◽  
Mengqi Jiang ◽  
Kun Xiang ◽  
Yadi Yang ◽  
Handong Huang
Keyword(s):  
Spatial Distribution ◽  
Acoustic Impedance ◽  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Well Log ◽  
Gas Reservoirs ◽  
Gas Reservoir ◽  
Absorption Effect ◽  
Impedance Inversion ◽  
Gas Bearing

Gas reservoir characterization is one of the frontiers in seismic exploration. Acoustic impedance, one of the most effective seismic attributes, aims to describe the spatial distribution of rock properties. However, using acoustic impedance only is insufficient to describe gas-bearing layers accurately, in the case of rapid lithologic changes and complex geology in the deepwater area. The reflection seismograms show an absorption effect when seismic waves propagate through gas-bearing layers. The absorption effect can be used as an additional attribute to help gas reservoir characterization. Therefore, a new attribute is proposed for gas reservoir characterization in this study, which integrates the results of acoustic impedance and absorption coefficient. We estimate the acoustic impedance model by employing poststack impedance inversion and then we calculate probability distribution functions. Functions are classified into gas-bearing and non-gas layers. We discuss an absorption coefficient and obtain it from the spectrum gradient, where the gradient is calculated by spectral decomposition using the matching pursuit method. We apply the new attribute to characterize the spatial distribution and thickness of deepwater gas reservoirs in the Pearl River Mouth Basin. Well-log and geologic information show that the study area has an enrichment of gas reservoirs. Field data application shows the explicit distribution of the gas reservoir and in accordance with the well-log information, which indicates that the proposed attribute can improve gas reservoir characterization.

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