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Chen, Yangkang (University of Texas–Austin) | Xiang, Kui (China University of Petroleum–Beijing) | Chen, Hanming (China University of Petroleum–Beijing) | Chen, Xiaohong (China University of Petroleum–Beijing)

Full waveform inversion (FWI) is a promising technique for inverting a high-resolution subsurface velocity model. The success of FWI highly depends on a fairly well initial velocity model. We propose a method for building a remarkable initial velocity model that can be put into the FWI framework for inverting nearly perfect velocity structure. We use a well log interpolated velocity model as a high-fidelity initial model for the subsequent FWI. The interpolation problem is solved via a least-squares method with a structural regularization. In order to obtain the geological structure of subsurface reflectors, an initial reverse time migration (RTM) with a fairly realistic initial velocity model is used to roughly calculate the local slope of subsurface structure. The well log interpolated initial velocity model can be very close to the true velocity while having small velocity anomaly or over-smoothing caused by the imperfect velocity interpolation, which however can be compensated during the subsequent FWI iterations. Regarding the field deployment, we suggest that future drilling should be seismic-oriented, which can help fully utilize the well logs for building initial subsurface velocity model and will facilitate a wide application of the proposed methodology.

Presentation Date: Wednesday, October 19, 2016

Start Time: 4:25:00 PM

Location: 162/164

Presentation Type: ORAL

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Chen, Yangkang (University of Texas—Austin) | Xiang, Kui (China University of Petroleum—Beijing) | Chen, Hanming (China University of Petroleum—Beijing) | Chen, Xiaohong (China University of Petroleum—Beijing)

The simultaneous-source shooting technique can accelerate field acquisition and improve spatial sampling but will cause strong interferences in the recorded data. Direct imaging of blended simultaneous-source data has been demonstrated to be a promising research field since there is no need to separate the blended data before the subsequent processing and imaging. The key issue in direct imaging of blended data is the strong artifacts in the migrated image. Although the least-squares migration can help reduce some artifacts, there are still residual artifacts in the image. Those artifacts mainly appear in the shallow part of the image as spatially incoherent noise. Previously proposed structural smoothing operator can effectively attenuate the artifacts for relatively simple reflection structures during least-squares inversion, but it will cause damage to complicated reflection events such as the discontinuities. In order to preserve the discontinuities in the seismic image, we apply the singular spectrum analysis (SSA) operator to attenuate artifacts during least-squares inversion. Considering that global SSA cannot deal with over-complicated data well, we propose to use local SSA in order to remove noise and preserve steeply dipping components better. The local SSA operator corresponds to a local low-rank constraint applied in the inversion process. The migration operator used in the study is the reverse time migration (RTM) operator. We use the Marmousi model example to show the superior performance of the proposed algorithm.

Presentation Date: Wednesday, October 19, 2016

Start Time: 11:35:00 AM

Location: 171/173

Presentation Type: ORAL

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Chen, Rukang (China University of Petroleum–Beijing) | Chen, Xiaohong (China University of Petroleum–Beijing) | Li, Jingye (China University of Petroleum–Beijing) | Wang, Zhikai (China University of Petroleum–Beijing) | Wang, Benfeng (Tsinghua University)

The direct hydrocarbon detection with seismic data is a difficult and longstanding problem. Numerous fluid indicators derived from linearized Zoeppritz equation have been published, which provide a good tool to identify hydrocarbon zones. But the question of which is these indicators take less advantages of frequency-dependent properties. In essence, rocks saturated with gas show high attenuation and wave dispersion, so the hydrocarbon indicators based on the frequency of the reflections can be used to improve estimation accuracy of hydrocarbon zones. In this abstract, we propose a new frequency-dependent indicator, which is derived from linearized FAVO inversion and traditional AVO methods. We apply this scheme to a real seismic data, and the results demonstrate that this new indicator can more accurately discriminate the gas/oil sand from the background and is also less sensitive to random noise and the accuracy of spectrum decomposition.

Presentation Date: Monday, October 17, 2016

Start Time: 3:20:00 PM

Location: 174

Presentation Type: ORAL

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

**Summary**

Full Waveform Inversion (FWI) has been regarded as an effective tool to build the velocity model for the following pre-stack depth migration. While traditional methods, which are built on the Born approximation, are initial model dependent. Introducing Transmission matrix (Tmatrix), which includes all orders of scattering effects, can avoid the initial model dependence. From the T-matrix to estimate the velocity perturbation, it requires matrix inversion which is always time consuming. In order to achieve that efficiently, previously we have proposed Inverse Thin-Slab Propagator (ITSP) which is suitable for smooth media, and we study domain decomposition strategy to estimate the velocity perturbation efficiently in this abstract. Numerical examples demonstrate the validity of the proposed method.

**Introduction**

As the development of seismic exploration and exploitation, it requires more and more accurate seismic processing technologies. Full waveform inversion (FWI) can provide accurate parameter distributions of the sub-surface media, while it is always time consuming and initial model dependent (Virieux and Operto, 2009). In order to improve the inversion efficiency, the GPU, phase encoding and source encoding technologies are adopted (Ben-Hadj-Ali et al., 2009; Luo et al., 2012). In order to weaken the initial model dependence, many authors have done much work. Bunks et al. (Bunks et al., 1995) proposed a multi-scale seismic waveform inversion strategy: the result from long scale seismic data is regarded as the initial model for the short scale seismic data which can weaken the initial model dependence. Shin and Cha (Shin and Cha, 2008; Shin and Cha, 2009) proposed Laplace domain and Laplace-Fourier domain waveform inversion strategy to provide initial model for the following FWI. Wu et al (Luo and Wu, 2015; Wu et al., 2014) proposed envelope inversion strategy, which can use the ultra low frequency components compared with source frequency band, to provide initial model for FWI. While these methods are all built on born approximation and the differences lie in the objective functions.

**Summary**

Observed seismic data is always irregular and seismic data interpolation is an essential procedure to provide accurate complete data for Surface Related Multiple Elimination (SRME) and wave equation based migration and inversion. While most interpolation methods belong to iterative method, how to define reconstruction error reasonably for terminating iterations duly is important for efficient seismic data interpolation. In this abstract, Projection Onto Convex Sets (POCS) method is achieved through the view of Iterative Hard Threshold (IHT) method and a novel reconstruction error definition is proposed with the information related with the observed seismic data. Tests on synthetic and real datasets demonstrate the validity of the proposed method.

** Introduction**

Acquired seismic data is always irregular sampled in spatial coordinates because of the presence of obstacles, forbidden areas, feathering and dead traces. Since multi-channel processing techniques, such as Surface Related Multiple Elimination (SRME), wave-equation based migration and inversion, require complete seismic data, seismic data interpolation technique, which can provide accurate complete seismic data for these multi-channel processing methods, is becoming an essential stage.

Interpolation methods can be divided into four categories (Gao et al., 2012; Wang et al., 2014): mathematical transform-based methods, prediction filtering-based methods, wave equation-based methods and rank reduction -based methods. While most interpolation methods belong to the category of iterative methods, the reconstruction error definition becomes essential for efficient seismic data interpolation. Gao et al. (2012) gave two reconstruction error definitions to monitor the convergence of the iterative interpolation methods: the first one uses the original complete seismic data and can only be used in theoretical research; the second one uses the adjacent iterative solutions and may trap in local minimum which leads to unsatisfactory interpolation results.

In this abstract, firstly, Projection Onto Convex Sets (POCS) method is achieved in the view of Iterative Hard Threshold (IHT) method; secondly, a novel reconstruction error definition is proposed with the information related with the observed seismic data. Tests on synthetic and real data prove the validity of the proposed method.

**Summary**

Projection Onto Convex Sets (POCS) method is an efficient iterative method for seismic data interpolation. In each iteration, observed seismic data is inserted into the updated solution, therefore it has difficulty for interpolation in noisy situations. Weighted POCS method can weaken the noise effects because it uses a weight factor to scale the observed seismic data, then fewer noisy data is inserted into the updated solution, but it still inserts some random noise. In this abstract, a novel method is proposed by combining the advantages of the weighted POCS method and the Iterative Hard Threshold (IHT) method: the weighted POCS method used for interpolation and the IHT method used for random noise elimination. The novel method can be used for simultaneous interpolation and random noise removal of seismic data, and its validity is demonstrated on synthetic and real datasets.

**Introduction**

Spatial irregularity and random noise observed in seismic data can affect the performance of Surface-Related Multiple Elimination (SRME), wave-equation based migration and inversion. Therefore, interpolation and random noise elimination is pre-requisite for multi-channel processing techniques.

Interpolation methods can be divided into four categories (Gao et al., 2012; Wang et al., 2014): mathematical transform based methods, prediction filters based methods, wave-equation based methods and rank-reduction based methods. Among these methods, mathematical transform based methods are easy to implement and have drawn much attention. While the random noise in observed seismic data can affect the interpolation performance and the irregularity of observed data can also affect the results of random noise elimination. Therefore, simultaneous interpolation and random noise attenuation is developed (Naghizadeh, 2012; Oropeza and Sacchi, 2011), while it is suitable for linear or quasi-linear events and should be handled window by window for curved events.

Gan, Shuwei (China University of Petroleum, Beijing) | Wang, Shoudong (China University of Petroleum, Beijing) | Chen, Yangkang (The University of Texas at Austin) | Chen, Xiaohong (China University of Petroleum, Beijing)

**Summary**

The distance separated simultaneous sourcing technique can make the interference between different source smallest. In a distance separated simultaneous-source acquisition system with two sources, we propose to use a novel iterative seisletframe thresholding approach to separate the blended data. Because the separation is implemented in common shot gathers, there is no need for the random scheduling that was used in conventional simultaneous-source acquisition, where the random scheduling is applied to ensure the incoherent property of blending noise in common midpoint, common receiver, or common offset gathers. Thus, the distance separated simultaneous sourcing becomes more flexible. The separation is based on the assumption that the local dips of the data from different sources are different. We can use plane-wave destruction (PWD) operator to simultaneously estimate the conflicting dips and then use seislet frames with two corresponding local dips to sparsify each signal component. The interference becomes unpredictable noise in the dip-governing seislet transform domain and can thus be removed by soft thresholding. A simulated field data example shows excellent performance of the proposed approach.

**Introduction**

The principal purpose of simultaneous source acquisition is to faster the acquisition of a larger-density seismic dataset, which saves numerous acquisition cost and increases data quality. The benefits are compromised by the intense interference between different shots (Berkhout, 2008). One way for solving the problem caused by interference is by first-separating and second-processing strategy (Chen et al., 2014a), which is also called deblending (Moore et al., 2008; Akerberg et al., 2008; Moore, 2010; Abma et al., 2010; Huo et al., 2012; Mahdad et al., 2011; Blacquiere and Mahdad, 2012; Beasley et al., 2012; Doulgeris et al., 2012; Mahdad et al., 2012; Bagaini et al., 2012; Li et al., 2013; Chen and Ma, 2014; Chen et al., 2014b; Berkhout and Blacquiere, 2014; Chen, 2014). Another way is by direct imaging and inversion of the blended data by attenuating the interference during inversion process (Verschuur and Berkhout, 2011; Dai and Schuster, 2011; Xue et al., 2014; Chen et al., 2015). Currently, deblending is still the dominant way for dealing with simultaneous-source data.

Gan, Shuwei (China University of Petroleum, Beijing) | Wang, Shoudong (China University of Petroleum, Beijing) | Chen, Yangkang (The University of Texas at Austin) | Chen, Xiaohong (China University of Petroleum, Beijing)

**Summary**

According to the compressive sensing (CS) theory in the signal-processing field, we proposed a new seismic data reconstruction approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain. The FPOCS can obtain much faster convergence than conventional POCS (about two thirds of conventional iterations can be saved). The seislet transform based reconstruction approach can achieve obviously better data recovery results than *f –k* transform based scenarios, considering both signal-to-noise ratio (SNR) and visual observation, because of a much sparser structure in the seislet transform domain. Both synthetic and field data examples demonstrate the performance of the proposed approach.

**Introduction**

Due to different reasons, seismic data may have missing traces. Seismic data reconstruction is such a procedure to remove sampling artifacts, and to improve amplitude analysis, which is very important for subsequent processing steps including highresolution processing, wave-equation migration, multiple suppression, amplitude-versus-offset (AVO) or amplitude-versusazimuth (AVAZ) analysis, and time-lapse studies (Trad et al., 2002; Liu and Sacchi, 2004; Abma and Kabir, 2005, 2006; Wang et al., 2010; Naghizadeh and Sacchi, 2010; Li et al., 2012, 2013; Chen et al., 2014a).

In recent years, because of the popularity of compressive sensing (CS) based applications (Cand`es et al., 2006b), there exists a new paradigm for seismic data acquisition that can potentially reduce the survey time and increase the data resolution (Herrmann, 2010). Compressive sensing (CS) is a relatively new paradigm in signal processing that has recently received a lot of attention. The theory indicates that the signal which is sparse under some basis may still be recovered even though the number of measurements is deemed insufficient by Shannon’s criterion. The principle of CS involves solving a least-square minimization problem with a *L*_{1} norm penalty term of the reconstructed model, which requires compromising a least-square data-misfit constraint and a sparsity constraint over the reconstructed model. The iterative shrinkage thresholding (IST) and the projection onto convex sets (POCS) are two common approaches used to solve the minimization problem in the exploration geophysics field.

SPE Disciplines:

**Summary**

We propose to use a structural-oriented median filter to attenuate the blending noise along the structural direction. The principle of the proposed approach is to first flatten the seismic record in local spatial windows and then to apply a traditional median filter (MF) to the third flattened dimension. The key component of the proposed approach is the estimation of the local slope, which can be calculated by first scanning the NMO velocity and then transferring the velocity to the local slope. Both synthetic and field data examples show successful performance using the proposed approach.

** Introduction**

The principal purpose of simultaneous source acquisition is to faster the acquisition of a larger-density seismic dataset, which saves numerous acquisition cost and increases data quality. The benefits are compromised by the intense interference between different shots (Berkhout, 2008). One way for solving the problem caused by interference is by first-separating and second-processing strategy (Chen et al., 2014a), which is also called deblending (Akerberg et al., 2008; Abma et al., 2010; Huo et al., 2012; Mahdad et al., 2011; Blacquiere and Mahdad, 2012; Beasley et al., 2012; Doulgeris et al., 2012; Mahdad et al., 2012; Bagaini et al., 2012; Li et al., 2013; Chen and Ma, 2014; Chen et al., 2014b; Berkhout and Blacquiere, 2014; Chen, 2014). Another way is by direct imaging and inversion of the blended data by attenuating the interference during inversion process (Verschuur and Berkhout, 2011; Dai and Schuster, 2011; Xue et al., 2014; Chen et al., 2015). Currently, deblending is still the dominant way for dealing with simultaneous-source data.

Robertson, Amy N. (National Renewable Energy Laboratory,) | Wendt, Fabian F. (National Renewable Energy Laboratory,) | Jonkman, Jason M. (National Renewable Energy Laboratory,) | Popko, Wojciech (Fraunhofer IWES) | Vorpahl, Fabian (Fraunhofer IWES) | Stansberg, Carl Trygve (Vorpahl Wind Engineering Consultants) | Bachynski, Erin E. (MARINTEK) | Bayati, Ilmas (MARINTEK) | Beyer, Friedemann (Politecnico di Milano) | de Vaal, Jacobus B. (University of Stuttgart) | Harries, Rob (Institute for Energy Technology) | Yamaguchi, Atshushi (DNV GL) | Shin, Hyunkyoung (University of Tokyo) | Kim, Byungcheol (University of Ulsan) | van der Zee, Tjeerd (University of Ulsan) | Bozonnet, Pauline (Knowledge Centre WMC) | Aguilo, Borja (IFP Energies nouvelles) | Bergua, Roger (Alstom Wind) | Qvist, Jacob (Alstom Wind) | Qijun, Wang (Subsea) | Chen, Xiaohong (Dongfang Turbine Co.) | Guerinel, Matthieu (ABS) | Tu, Ying (WavEC Offshore Renewables) | Yutong, Huang (Norwegian University of Science and Technology) | Li, Rongfu (Chinese General Certification) | Bouy, Ludovic (Goldwind)

**Abstract**

This paper describes work performed during the first half of Phase I of the Offshore Code Comparison Collaboration Continuation, with Correlation project (OC5). OC5 is a project run under the International Energy Agency Wind Research Task 30, and is focused on validating the tools used for modeling offshore wind systems. In this first phase, simulated responses from a variety of offshore wind modeling tools were validated against tank test data of a fixed, suspended cylinder (without a wind turbine) that was tested under regular and irregular wave conditions at MARINTEK. The results from this phase include an examination of different approaches one can use for defining and calibrating hydrodynamic coefficients for a model, and the importance of higher-order wave models in accurately modeling the hydrodynamic loads on offshore substructures.

SPE Disciplines: