Seismic inversion based on proximal objective function optimization algorithm

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. R237-R246 ◽  
Author(s):  
Ronghuo Dai ◽  
Fanchang Zhang ◽  
Hanqing Liu
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Computational Efficiency ◽  
Real Data ◽  
Cauchy Distribution ◽  
Critical Issue ◽  
Seismic Inversion ◽  
Function Optimization ◽  
Inversion Method ◽  
Reservoir Prediction

Seismic impedance inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is its low computational efficiency, especially in 3D. To improve the computational efficiency, we have developed an inversion method derived from the proximal objective function optimization algorithm. Our inversion method calculates each unknown parameter in the model vector, one by one during iteration. Compared with routine gradient-dependent inversion algorithms, such as the iteratively reweighted least-squares (IRLS) algorithm, our inversion method has lower computational complexity as well as higher efficiency. In addition, to obtain a sparse reflectivity series, a long-tailed Cauchy distribution is used as the a priori constraint. The weak nonlinear problem owing to the introduction of Cauchy sparse constraint is addressed by taking advantage of reweighting strategy. Results of synthetic and real data tests illustrate that the proposed inversion method has higher computational efficiency than IRLS algorithm, and its inversion accuracy remains the same.

Nonlinear amplitude-variation-with-offset inversion for Lamé parameters using a direct inversion method

2017 ◽  
Vol 5 (3) ◽  
pp. SL57-SL67 ◽  
Author(s):  
Guangsen Cheng ◽  
Xingyao Yin ◽  
Zhaoyun Zong
Keyword(s):  
Nonlinear Equation ◽  
Real Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Reservoir Prediction ◽  
Amplitude Variation ◽  
Linear Inversion ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

Prestack seismic inversion is widely used in fluid indication and reservoir prediction. Compared with linear inversion, nonlinear inversion is more precise and can be applied to high-contrast situations. The inversion results can be affected by the parameters’ sensitivity, so the parameterization of nonlinear equations is very significant. Considering the poor nonlinear amplitude-variation-with-offset (AVO) inversion results of impedance and velocity parameters, we adjust the parameters of the nonlinear equation, avoid the inaccuracy caused by parameters sensitivity and get the ideal nonlinear AVO inversion results of the Lamé parameters. The feasibility and stability of the nonlinear equation based on the Lamé parameters and method are verified by the model and the real data examples. The resolution and the lateral continuity of nonlinear inversion results are better compared with the linear inversion results.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

High resolution fixed-point seismic inversion

2021 ◽  
pp. 1-54
Author(s):  
Song Pei ◽  
Xingyao Yin ◽  
Zhaoyun Zong ◽  
Kun Li
Keyword(s):  
Fixed Point ◽  
Objective Function ◽  
Seismic Data ◽  
Target Function ◽  
Recursive Formula ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Good Convergence ◽  
Band Limited

Resolution improvement always presents the crucial task in geological inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geological inversion suffers from the deficiency of both low- and high-frequency components. We propose the fixed-point seismic inversion method to alleviate these issues. The problem of solving objective function is transformed into the problem of finding the fixed-point of objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to around zero after few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by the moderate noise, the optimal inverted results can still be obtained with the proposed method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition (SVD) method is applied to the inversion framework, thus making a high improvement of anti-noise ability. At last, the synthetic models and seismic data are investigated following the proposed method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

Application of Inversion after Limestone Hollow-Out in Pearl River Mouth Basin

2013 ◽  
Vol 734-737 ◽  
pp. 294-299
Author(s):  
Ying Jun Yang ◽  
Jun Mao Zheng ◽  
Zheng Qing Ding
Keyword(s):  
River Mouth ◽  
Prediction Method ◽  
Forward Modeling ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Sand Ridge ◽  
Pearl River Mouth Basin ◽  
Delta Front ◽  
Reservoir Prediction ◽  
Sand Bodies

The target layer of study area belongs to delta front and shallow shelf deposition, and the sedimentary formation is sandstone and mudstone thin interbed, the limy developed in sand layer. To make the inversion data reflect the characteristics of reservoir accurately, the methods of limy separation analysis by drilling and forward modeling have been used in this paper, and removed the limy from seismic data according to the characteristics of wave impedance（limestone hollow-out）. According to the research of limestone hollow-out Inversion, delta front sand bodies developed in the J2 formation of H district, and the J2 formation of H district where it is in the west of the study area, a gravity flow channel in the center, shallow shelf sand ridge sand bodies mainly developed in the eastern; delta front sand bodies developed in the J1 formation of HD district, which includes underwater branch channel sand body. A number of lithologic traps have been found and optimized by the research of Retrieval data near the large fault and ramp region, and also have achieved the intended purpose. This inversion method is a kind of reservoir prediction method based on seismic, geological and logging, which added in forward modeling techniques, and is an extension of Model seismic inversion method, and also Provided a new approach for reservoir prediction of the special lithological developed zone.

Seismic inversion with adaptive edge-preserving smoothing preconditioning on impedance model

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R11-R19 ◽  
Author(s):  
Ronghuo Dai ◽  
Cheng Yin ◽  
Nueraili Zaman ◽  
Fanchang Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Window Size ◽  
Seismic Inversion ◽  
Small Scale ◽  
Inversion Method ◽  
Reservoir Prediction ◽  
Edge Preserving ◽  
Impedance Model

Poststack seismic impedance inversion is an effective approach for reservoir prediction. Due to the sensitivity to noise and the oscillation near the bed boundary, Gaussian distribution constrained seismic inversion is unfavorable to delineate the subtle-reservoir and small-scale geologic features. To overcome this shortcoming, we have developed a new method that incorporates a priori knowledge in the seismic inversion through a preconditioning impedance model using the adaptive edge-preserving smoothing (Ad-EPS) filter. The Ad-EPS filter preconditioned impedance model for a blocky solution makes the formation interfaces and geologic edges more precise and sharper in the inverted impedance results and keeps the inversion procedure robust even if random noise exists in the seismic data. Furthermore, compared with the conventional EPS filter, the Ad-EPS filter is able to resolve thick and thin geologic features through window size scanning, which is used to find the best-fitting window size for each sample to be filtered. The results of numerical examples and real seismic data test indicate that our inversion method can suppress noise to obtain a “blocky” inversion result and preserve small geologic features.

A Weighting Function-Based Method for Resistivity Inversion in Subsurface Investigations

2020 ◽  
Vol 25 (1) ◽  
pp. 129-138
Author(s):  
Lichao Nie ◽  
Zhao Ma ◽  
Bin Liu ◽  
Zhenhao Xu ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Objective Function ◽  
Weighting Function ◽  
Real Data ◽  
Weighting Factor ◽  
Inversion Method ◽  
Depth Information ◽  
Detection Accuracy ◽  
Resistivity Inversion ◽  
Inversion Procedure ◽  
Anomalous Bodies

There is a high demand for high detection accuracy and resolution with respect to anomalous bodies due to the increased development of underground spaces. This study focused on the weighted inversion of observed data from individual array type electrical resistivity tomography (ERT), and developed an improved method of applying a data weighing function to the geoelectrical inversion procedure. In this method, the weighting factor as an observed data weighting term was introduced into the objective function. For individual arrays, the sensitivity decreases with increasing electrode interval. Therefore, the Jacobian matrices were computed for the observed data of individual arrays to determine the value of the weighting factor, and the weighting factor was calculated automatically during inversion. In this work, 2D combined inversion of ERT data from four-electrode Alfa-type arrays is examined. The effectiveness of the weighted inversion method was demonstrated using various synthetic and real data examples. The results indicated that the inversion method based on observed data weighted function could improve the contribution of observed data with depth information to the objective function. It has been proven that the combined weighted inversion method could be a feasible tool for improving the accuracies of positioning and resolution while imaging deep anomalous bodies in the subsurface.

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.

Model reduction in geostatistical seismic inversion with functional data analysis

Geophysics ◽  
2021 ◽  
pp. 1-48
Author(s):  
Leonardo Azevedo
Keyword(s):  
Data Analysis ◽  
Model Reduction ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Computational Cost ◽  
Real Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Inversion Methods ◽  
Starting Point

In subsurface modelling and characterization, predicting the spatial distribution of subsurface elastic properties is commonly achieved by seismic inversion. Stochastic seismic inversion methods, such as iterative geostatistical seismic inversion, are widely applied to this end. Global iterative geostatistical seismic inversion methods are computationally expensive as they require, at a given iteration, the stochastic sequential simulation of the entire inversion grid at once multiple times. Functional data analysis is a well-established statistical method suited to model long-term and noisy temporal series. This method allows to summarize spatiotemporal series in a set of analytical functions with a low-dimension representation. Functional data analysis has been recently extended to problems related to geosciences, but its application to geophysics is still limited. We propose the use functional data analysis as a model reduction technique during the model perturbation step in global iterative geostatistical seismic inversion. Functional data analysis is used to collapse the vertical dimension of the inversion grid. We illustrate the proposed hybrid inversion method with its application to three-dimensional synthetic and real data sets. The results show the ability of the proposed inversion methodology to predict smooth inverted subsurface models that match the observed data at a similar convergence as obtained by a global iterative geostatistical seismic inversion, but with a considerable decrease in the computational cost. While the resolution of the inverted models might not be enough for a detailed subsurface characterization, the inverted models can be used as starting point of global iterative geostatistical seismic inversion to speed-up the inversion or to test alternative geological scenarios by changing the inversion parameterization and obtaining inverted models in a relatively short time.

scholarly journals Prestack Seismic Inversion via Nonconvex L1-2 Regularization

2021 ◽  
Vol 11 (24) ◽  
pp. 12015
Author(s):  
Wenliang Nie ◽  
Fei Xiang ◽  
Bo Li ◽  
Xiaotao Wen ◽  
Xiangfei Nie
Keyword(s):  
Objective Function ◽  
Regularization Method ◽  
Oil And Gas ◽  
A Priori ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Data Logging ◽  
Elastic Parameters ◽  
Geological Interpretation

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L1−2 regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L1−2 regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L1−2 regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs.

Data-driven multichannel poststack seismic impedance inversion via patch-ordering regularization

Geophysics ◽  
2020 ◽  
pp. 1-93
Author(s):  
Lingqian Wang ◽  
Hui Zhou ◽  
Wenling Liu ◽  
Bo Yu ◽  
Huili He ◽  
...  
Keyword(s):  
Acoustic Impedance ◽  
Random Noise ◽  
Seismic Profile ◽  
Seismic Inversion ◽  
Data Driven ◽  
Inversion Method ◽  
Reservoir Prediction ◽  
Spatial Continuity ◽  
Impedance Inversion ◽  
Acoustic Impedance Inversion

Seismic acoustic impedance inversion plays an important role in reservoir prediction. However, single-trace inversion methods often suffer from spatial discontinuities and instability due to the poor-quality seismic records with spatially variable signal-to-noise ratio or missing traces. The specified hyper parameters for seismic inversion cannot be suitable to all seismic traces and subsurface structures. In addition, conventional multichannel inversion imposes lateral continuity with a pre-specified mathematical model. However, the inversion results constrained with specified lateral regularization are inferior when the subsurface situations violate the hypothesis. A data-driven multichannel acoustic impedance inversion method with patch-ordering regularization is introduced, where the spatial correlation of seismic reflection is utilized. The method decomposes the seismic profile into patches and constructs the patch-ordering matrix based on the similarity among seismic patches to record the impedance structural extension. So the patch-ordering matrix can record the spatial extension of the acoustic impedance. Then, a simple regularization with difference operator of varying weights can reduce the random noise presented in the inverted impedance profile, stabilize the inversion result and enhance the spatial continuity of layer extension. The objective function for multichannel poststack seismic impedance inversion can be constructed by integrating the observed seismic record and the spatial continuity in the form of patch-ordering regularization, and be solved effectively with Limited-Memory BFGS algorithm. The synthetic and field data tests illustrate the improvement of accuracy and lateral continuity of inverted results with our method, compared to conventional model-based inversion results.

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