Accurate Pore Fluid Indicator Prediction Using Seismic Fluid Bulk Modulus Inversion

2021 ◽  
Author(s):  
Makky Sandra Jaya ◽  
Ghazali Ahmad Riza ◽  
Ahmad Fuad M. Izzuljad ◽  
Mad Sahad Salbiah
Keyword(s):  
Bulk Modulus ◽  
Seismic Data ◽  
High Probability ◽  
Quantitative Measure ◽  
Seismic Inversion ◽  
Pore Fluid ◽  
Blue Color ◽  
Direct Estimation ◽  
Inversion Technique ◽  
Fluid Parameter

Submitted Abstract Objectives/Scope The prediction of fluid parameter related to hydrocarbon presence using seismic data has often been limited by the performance of probability density function in estimating fluid properties from seismic inversion results. A novel fluid bulk modulus inversion (fBMI) is a pre-stack seismic inversion technique that has been developed to allow a direct estimation of pore fluid bulk modulus (Kf) from seismic data. Real data application in Malay basin showcases that Kf volume can be used to pinpoint areas with high probability of hydrocarbon presence. Methods, Procedures, Process The fluid term AVO reflectivity (Russell et al., 2011) is used as the basis of our formulation and has been extended to allow direct estimation of pore fluid bulk modulus, shearmodulus, porosity parameter and density through standard least-square inversion. The novel formulation is able to relax the dependency of fluid terms on the porosity. To demonstrate this, verifications were made against standard linear AVO approximations. Our observation shows that the young tertiary basins such as the Malay basin the fluid bulk modulus values have a big contrast between hydrocarbon saturated and water bearing reservoirs with a minimum of 60% ratio difference. The inverted fluid bulk modulus volume provides thus a direct assessment of areas with high probability of hydrocarbon saturation. Results, Observations, Conclusions In this paper, the fBMI technique is showcased on a field in the Malay basin. The outcome is demonstrated on a well panel analysis for four wells located across the study area (Figure 1). The inverted fluid bulk modulus extracted along a horizon representing the top of target reservoir is shown in Figure 2b. The blue color indicates high bulk modulus corresponds to water-bearing zone, while the yellow-red color range corresponding to low hydrocarbon-bearing zones. The areas of low fluid bulk modulus values at the north-western region are calibrated to known production zones in that region. fBMI shows areas that delineate high probability of hydrocarbon presence and provides a quantitative measure in terms of fluid parameter directly related to the presence of hydrocarbon saturations. Figure 1: Comparison analysis of water saturation (blue curve) and fluid bulk modulus (red curve) of well log data in the Malay basin. Black strips indicate the coal intervals. Figure 2: a) Inverted acoustic impedance extracted from the top reservoir horizon of a field in the Malay basin. b) The corresponding fluid bulk modulus values from fBMI. Novel/Additive Information The fBMI is a new four parameters linear amplitude-versus-offset inversion technique that provides quantitative fluid parameter directly related to fluid bulk modulus from seismic data. It is utilized as a tool for direct hydrocarbon prospect assessment to differentiate gas, oil, condensate and water.

scholarly journals High-Order AVO Inversion for Effective Pore-Fluid Bulk Modulus Based on Series Reversion and Bayesian Theory

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1313
Author(s):  
Lei Shi ◽  
Yuhang Sun ◽  
Yang Liu ◽  
David Cova ◽  
Junzhou Liu
Keyword(s):  
Bulk Modulus ◽  
Seismic Data ◽  
Pore Fluid ◽  
High Order ◽  
Bayesian Theory ◽  
P Wave ◽  
Seismic Exploration ◽  
Inversion Method ◽  
Fluid Identification ◽  
Avo Inversion

Pore-fluid identification is one of the key technologies in seismic exploration. Fluid indicators play important roles in pore-fluid identification. For sandstone reservoirs, the effective pore-fluid bulk modulus is more susceptible to pore-fluid than other fluid indicators. AVO (amplitude variation with offset) inversion is an effective way to obtain fluid indicators from seismic data directly. Nevertheless, current methods lack a high-order AVO equation for a direct, effective pore-fluid bulk modulus inversion. Therefore, based on the Zoeppritz equations and Biot–Gassmann theory, we derived a high-order P-wave AVO approximation for an effective pore-fluid bulk modulus. Series reversion and Bayesian theory were introduced to establish a direct non-linear P-wave AVO inversion method. By adopting this method, the effective pore-fluid bulk modulus, porosity, and density can be inverted directly from seismic data. Numerical simulation results demonstrate the precision of our proposed method. Model and field data evaluations show that our method is stable and feasible.

Application of Seismic Inversion Technique Base on Geostatistics in Prediction of Thin Sand Body Reservoir

2014 ◽  
Vol 1030-1032 ◽  
pp. 724-727
Author(s):  
Chun Lei Li ◽  
Wen Qi Zhang ◽  
Zhao Hui Xia ◽  
Ming Zhang ◽  
Liang Chao Qu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Inversion Method ◽  
Reservoir Model ◽  
Reservoir Prediction ◽  
Inversion Technique ◽  
Pulse Inversion ◽  
Sand Body ◽  
Geostatistical Inversion

Seismic inversion methods include constrained sparse pulse inversion and band limit inversion, etc. Although resolution of the seismic inversion results is higher than seismic data, it does not identify thin interbedding sand body and confirm the development of reservoirs. In this paper, in A block of Indonesia adopted geostatistical inversion in reservoir prediction, which is a method of seismic inversion combining geological statistics simulation and seismic inversion. This inversion method can establish various 3D geological model with the same probability of rock properties and lithology and it obey all seismic, logging and geological data. Using statistical regularity and seismic inversion technique we can obtain more fine reservoir model and finally reach the purpose of identification of single thin sand layer.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

scholarly journals Batch seismic inversion using the iterative ensemble Kalman smoother

2021 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik
Keyword(s):  
Seismic Data ◽  
Time Windows ◽  
Seismic Inversion ◽  
Efficient Estimation ◽  
Solution Strategy ◽  
Adaptive Procedure ◽  
Waveform Data ◽  
Kalman Smoother ◽  
Seismic Waveform ◽  
Single Batch

AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

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