Seismic impedance inversion using dictionary learning-based sparse representation and nonlocal similarity

2019 ◽  
Vol 7 (3) ◽  
pp. SE51-SE67 ◽  
Author(s):  
Bin She ◽  
Yaojun Wang ◽  
Zhining Liu ◽  
Hanpeng Cai ◽  
Wei Liu ◽  
...  
Keyword(s):  
Objective Function ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Sparse Representations ◽  
Well Log ◽  
Log Data ◽  
Impedance Inversion ◽  
Learned Dictionary ◽  
Nonlocal Similarity

We have addressed the seismic impedance inversion problem, which is often ill posed because of inaccurate and insufficient data. The approach taken is based on dictionary learning and sparse representation. By shifting a patch window of fixed size on all well-log data, a large number of small overlapping patches are generated. Then regarding these patches as a training set and using K-singular value decomposition algorithm, we obtain a dictionary that describes the common features of subsurface models within the current survey area. On the basis of the assumption that the subsurface geology has similarity and lateral continuity to some extent, the dictionary is used to approximate each model via sparse representations over the learned dictionary. In particular, we impose the sparse representations as additional constraints into the inversion procedure, leading to a well-defined objective function that can not only fit the observed seismic data but also honor the features of the well-log data. We adopt a coordinate descent strategy to solve this objective function. Meanwhile, to enforce lateral continuity in the inverted models, we use an additional stage in which we use the nonlocal similarity information that is extracted from seismic data as spatial coherent prior knowledge to refine the estimated models. Compared with several traditional impedance inversion methods, our algorithm can produce solutions of much higher quality qualitatively and quantitatively.

scholarly journals A method of combining coherence-constrained sparse coding and dictionary learning for denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V137-V148 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Seismic Signal ◽  
Noise Variance ◽  
A Priori Information ◽  
Seismic Section ◽  
Learned Dictionary ◽  
Priori Information

We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.

Structurally constrained impedance inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T577-T589 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky
Keyword(s):  
Objective Function ◽  
Seismic Data ◽  
Field Data ◽  
A Priori ◽  
Structural Constraints ◽  
Inversion Algorithm ◽  
Data Set ◽  
Impedance Inversion ◽  
Single Trace ◽  
Complex Geology

In complex geology, the presence of highly dipping structures can complicate impedance inversion. We have developed a structurally constrained inversion in which a computationally well-behaved objective function is minimized subject to structural constraints. This approach allows the objective function to incorporate structural orientation in the form of dips into our inversion algorithm. Our method involves a multitrace impedance inversion and a rotation of an orthogonal system of derivative operators. Local dips used to constrain the derivative operators were estimated from migrated seismic data. In addition to imposing structural constraints on the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We investigated this algorithm on a complex synthetic 2D model as well as a seismic field data set. We compared the result obtained with this approach with the results from single trace-based inversion and laterally constrained inversion. The inversion carried out using dip information produces a model that has higher resolution that is more geologically realistic compared with other methods.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Identification of Natural Gas Hydrates in Blocks 25a, 25b, 26 and 27 Offshore Trinidad Using Available Well Log Data

2014 ◽  
Author(s):  
M. M. Smith ◽  
L. E. Sobers
Keyword(s):  
Natural Gas ◽  
Deep Water ◽  
Gas Hydrates ◽  
Seismic Data ◽  
East Coast ◽  
Depositional Environments ◽  
Well Log ◽  
Borehole Data ◽  
Log Data ◽  
Natural Gas Hydrates

Abstract Natural gas hydrates can be found in conventional hydrocarbon depositional environments such as clastic marine sediments, siltstones and unconsolidated sands and in oceanic environments for reservoir pressures greater than 663 psi (46 bars) and temperatures less than 20 °C. These conditions are found in the deep water (> 300 m) acreage off the South East coast of Trinidad. Natural gas hydrates have been recovered in this area during drilling and seismic data have shown that there may be deposits in some areas. In this study we reviewed all the available borehole data and employed well log interpretation techniques to identify natural gas hydrates in the deep water acreage blocks 25 a, 25 b, 26 and 27 off the Trinidad South East coast. The analysis of well log data for the given depths did not present evidence to suggest the presence of natural gas hydrates in Blocks 25 a, 25 b, 26 and 27. In this paper we present our analysis of the data available and recommend the formation depths which should be logged in during the deep water exploration drilling to confirm the seismic data and core data which indicate the presence of natural gas hydrates in these blocks.

scholarly journals Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 943-958 ◽  
Author(s):  
Xènia Ogaya ◽  
Juan Alcalde ◽  
Ignacio Marzán ◽  
Juanjo Ledo ◽  
Pilar Queralt ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Unconsolidated Sediments ◽  
Well Log ◽  
Storage Site ◽  
Near Surface ◽  
Log Data ◽  
Velocity Models ◽  
Joint Interpretation

Abstract. Hontomín (N of Spain) hosts the first Spanish CO2 storage pilot plant. The subsurface characterization of the site included the acquisition of a 3-D seismic reflection and a circumscribed 3-D magnetotelluric (MT) survey. This paper addresses the combination of the seismic and MT results, together with the available well-log data, in order to achieve a better characterization of the Hontomín subsurface. We compare the structural model obtained from the interpretation of the seismic data with the geoelectrical model resulting from the MT data. The models correlate well in the surroundings of the CO2 injection area with the major structural differences observed related to the presence of faults. The combination of the two methods allowed a more detailed characterization of the faults, defining their geometry, and fluid flow characteristics, which are key for the risk assessment of the storage site. Moreover, we use the well-log data of the existing wells to derive resistivity–velocity relationships for the subsurface and compute a 3-D velocity model of the site using the 3-D resistivity model as a reference. The derived velocity model is compared to both the predicted and logged velocity in the injection and monitoring wells, for an overall assessment of the computed resistivity–velocity relationships. The major differences observed are explained by the different resolution of the compared geophysical methods. Finally, the derived velocity model for the near surface is compared with the velocity model used for the static corrections in the seismic data. The results allowed extracting information about the characteristics of the shallow unconsolidated sediments, suggesting possible clay and water content variations. The good correlation of the velocity models derived from the resistivity–velocity relationships and the well-log data demonstrate the potential of the combination of the two methods for characterizing the subsurface, in terms of its physical properties (velocity, resistivity) and structural/reservoir characteristics. This work explores the compatibility of the seismic and magnetotelluric methods across scales highlighting the importance of joint interpretation in near surface and reservoir characterization.

Seismic impedance inversion using dictionary learning and sparse representation

2018 ◽  
Author(s):  
Bin She ◽  
Kunhong Li ◽  
Zhining Liu ◽  
Yaojun Wang ◽  
Hanpeng Cai ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Impedance Inversion

Broadband seismic amplitude variation with offset inversion

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. M43-M53 ◽  
Author(s):  
Zhaoyun Zong ◽  
Kun Li ◽  
Xingyao Yin ◽  
Ming Zhu ◽  
Jiayuan Du ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Frequency Domain ◽  
Seismic Data ◽  
Low Frequency ◽  
Well Log ◽  
Complex Frequency ◽  
Elastic Parameters ◽  
Log Data ◽  
Avo Inversion ◽  
Frequency Components

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

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