**Source**

**Conference**

- 1998 SEG Annual Meeting (3)
- 2004 SEG Annual Meeting (1)
- 2005 SEG Annual Meeting (1)
- 2007 SEG Annual Meeting (1)
- 2008 SEG Annual Meeting (2)
- 2009 SEG Annual Meeting (3)
- 2010 SEG Annual Meeting (3)
- 2011 SEG Annual Meeting (2)
- 2012 SEG Annual Meeting (1)
- 2013 SEG Annual Meeting (8)
- 2014 SEG Annual Meeting (15)
- 2015 SEG Annual Meeting (5)
- 2016 SEG International Exposition and Annual Meeting (7)
- 2017 SEG International Exposition and Annual Meeting (11)
- SEG International Exposition and Annual Meeting (6)

**Theme**

**Author**

- Cao, Danping (6)
- Chen, Huaizhen (7)
- Chen, Jiaojiao (1)
- Chen, Jin (1)
- Cheng, Guangsen (1)
- Danping, Cao (1)
- Du, Jiayuan (1)
- Guangsen, Cheng (1)
- Guo, Junchao (1)
- Han, Xuanying (1)
- Hao, Xuejing (1)
- He, Dongyang (1)
- Hongxue, Zhang (1)
- Lei, Ting (1)
- Li, Chao (2)
- Li, Kun (11)
- Liang, Kai (1)
- Lin, Liming (1)
- Liu, Bo (1)
- Liu, Chanjuan (1)
- Liu, Daoli (1)
- Liu, Hanqing (1)
- Liu, Qian (3)
- Liu, Xiaojing (3)
- Liu, Xinxin (1)
- Liu, Yongshe (1)
- Lu, Li (1)
- Pu, Yitao (1)
- Pu, Yong (1)
- Qu, Shouli (2)
- Sun, Ruiying (3)
- Sun, Wenguo (1)
- Tang, Yunwei (1)
- Valenciano, Alejandro (1)
- Wang, Baoli (5)
- Wang, Huixin (1)
- Wang, Li (1)
- Wang, Qi (1)
- Wang, Xiaodan (1)
- Wang, Xiaojie (1)
- Wu, Guochen (9)
- Xiao, Yanan (1)
- Yang, Fengying (1)
- Yang, Fusen (1)
- Yang, Peijie (2)
**Yin, Xingyao (69)**- Zhang, Fanchang (5)
- Zhang, Guangzhi (23)
- Zhang, Shixin (4)
- Zhao, Xiaolong (2)
- Zhao, Zhengyang (1)
- Zheng, Jingjing (2)
- Zheng, Ying (3)
- Zhou, Dongyong (1)
- Zhou, Jianke (1)
- Zhou, Qichao (1)
- Zhou, Qijie (1)
- Zhu, Ming (1)
- Zong, Zhaoyun (23)

**Concept Tag**

- acoustic impedance (2)
- algorithm (7)
- analysis (3)
- anisotropic gradient (3)
- annual meeting (2)
- application (4)
- approximation (7)
- Artificial Intelligence (38)
- attenuation (4)
- AVA inversion (2)
- AVAZ inversion (5)
- AVO inversion (10)
- AVO inversion method (2)
- azimuth (2)
- band (2)
- basis Pursuit (2)
- basis pursuit inversion (2)
- Bayesian (2)
- Bayesian Inference (14)
- Bayesian inversion (3)
- boundary (3)
- carbonate (2)
- carbonate rock (2)
- Cauchy (2)
- characteristic (2)
- coefficient (4)
- constraint (10)
- crosswell seismic (3)
- curvelet (2)
- Curvelet transform (2)
- deep reservoir (2)
- digital core (2)
- discrimination (2)
- dispersion (2)
- distribution (5)
- Efficiency (2)
- elastic impedance (6)
- elastic impedance equation (2)
- elastic parameter (8)
- equation (25)
- estimation (11)
- evolutionary algorithm (3)
- fluid discrimination (5)
- fluid factor (7)
- fracture (6)
- frequency (13)
- function (3)
- Gaussian (3)
- geophysics (4)
- impedance (11)
- impedance inversion (3)
- incident angle (6)
- information (19)
- initial model (4)
- inversion (50)
- inversion method (13)
- inversion result (19)
- log analysis (3)
- machine learning (23)
- matrix (6)
- media (3)
- method (6)
- Metropolis (2)
- model (3)
- model parameter (3)
- modeling (3)
- noise (4)
- null (3)
- optimization problem (4)
- porosity (4)
- prestack (3)
- probability (6)
- reflection (3)
- reflection coefficient (10)
- reflectivity (12)
- regularization (3)
- Reservoir Characterization (68)
- reservoir description and dynamics (14)
- resolution (7)
- rock physics (5)
- s-wave velocity (6)
- SEG (2)
- seismic data (15)
- seismic inversion (9)
- seismic processing and interpretation (14)
- shale (4)
- shear modulus (4)
- Simulation (7)
- sparse (3)
- stochastic inversion (7)
- surface seismic (3)
- time domain (3)
- transform (3)
- Upstream Oil & Gas (69)
- Velocity Dispersion (2)
- VSP (2)
- wavelet (8)
- weakness (3)
- well (4)
- well logging (3)

**File Type**

Li, Kun (China University of Petroleum (East China)) | Zhu, Ming (Shenzhen Branch of CNOOC Ltd) | Du, Jiayuan (Shenzhen Branch of CNOOC Ltd) | Liu, Daoli (Shenzhen Branch of CNOOC Ltd) | Yin, Xingyao (China University of Petroleum (East China)) | Zong, Zhaoyun (China University of Petroleum (East China))

Lithology prediction and geofluid discrimination are the ultimate objectives of rock physical analysis and prestack seismic inversion. For prestack Bayesian estimation and geostatistical simulation, the prior probability density distribution of model parameters are usually influenced by subsurface lithologies and geofluid facies, which consist of several Gaussian probability components with different means and covariances. With the assumption of Gaussian mixture a priori, one improved prestack EVA inversion (elastic impedance variation with angle) conditioned by seismic and well data in mixed-domain is proposed to realize the estimation of discrete lithofacies and continuous geofluid parameters. The peaks number of prior Gaussian probability density is the same as classifications of sedimentary lithologies. For the resolution of seismic inversion, sequential simulation algorithm is utilized to sample the posterior probability distributions. Besides, the low frequency regularization and nonlinear bounding constraint strategy are introduced into the proposed method, which can enhance the stability of prestack EVA inversion and overcome the unrealistic solutions of elastic parameters. Finally, model tests and the applications on field prestack seismic data can verify the effectiveness and practicability in geofluid discrimination of the proposed algorithm.

Presentation Date: Thursday, October 18, 2018

Start Time: 8:30:00 AM

Location: 206A (Anaheim Convention Center)

Presentation Type: Oral

Artificial Intelligence, Bayesian Inference, constraint, direct estimation, elastic impedance, estimation, eva inversion, fluid term, frequency, gassmann fluid, Gaussian, Gaussian mixture, geofluid, geofluid parameter, inversion, lithofacies, machine learning, prestack, prestack eva inversion, probability, regularization, Reservoir Characterization, Upstream Oil & Gas

Industry:

- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > China Government (0.47)

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

He, Dongyang (China University of Petroleum-East China) | Yin, Xingyao (China University of Petroleum-East China) | Zong, Zhaoyun (China University of Petroleum-East China) | Li, Kun (China University of Petroleum-East China)

Summary Gaussian mixture model can be used to describe the multimodal behaviour of reservoir properties due to their variations within different discrete variables, such as facies. The weights of the Gaussian components represents the probabilities of the discrete variables. However, Bayesian linear inversion based on Gaussian mixture may misclassify discrete variables at some points, which may lead to a bad inversion result. In this study, we consider the spatial variability of discrete variables and combine Gaussian mixture model with the Sequential indicator simulation to determine the weight of each discrete variable in Sequential Bayesian linear inversion problems. We then can obtain the analytical solution of the Bayesian linear inverse problem and simultaneously classify the discrete variables.

SPE Disciplines:

Technology:

Markov Chains Monte Carlo is an important type of posterior probability sampling method in the probability inversion. However, the search process of algorithm is time consuming and often comes to the local optimal solution, which limited its application in the inverse problem of non-unique solutions. In view of the above limitation, this paper improves the traditional Metropolis-Hastings algorithm and proposes the GAMH algorithm based on the genetic crossover operation. The improved method is able to exchange information among multiple chains according to the search results, so as to search as many solutions as possible to reduce the possibility of global random walk into local solution and then improve the accuracy of inversion. This paper points out that in the process of managing model data, using GAMH-MCMC method in the AVO inversion is capable to obtain more accurate results than using the traditional method. Furthermore, GAMH-MCMC method can improve computational efficiency to some extent.

Presentation Date: Wednesday, October 17, 2018

Start Time: 1:50:00 PM

Location: 206A (Anaheim Convention Center)

Presentation Type: Oral

-wave velocity, algorithm, Artificial Intelligence, AVO inversion, Crossover, gamh, information, inversion, inversion result, machine learning, Markov chain, McMC method, model data, operation, posterior probability, probability, Reservoir Characterization, seismic record, stochastic inversion, trace time, traditional mh, Upstream Oil & Gas

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

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)

Reservoir fluid mobility can be used to determine location and spatial distribution of oil and gas reservoirs. However, little research has been conducted to show the direct calculating method to estimate the fluid mobility. A calculation approach of reservoir fluid mobility with frequency dependent inversion is proposed in this study. Firstly, we analyzed the various factors that affect the frequency-dependent reflectivity based on the asymptotic analysis theory of fluid-saturated poroelastic medium. Furthermore, the computation formula for fluid mobility with frequency dependent Bayesian inversion is deduced. We estimate the fluid mobility by solving the initial objective function with low frequency constraints to improve the inversion robustness. In addition, the synthetic examples demonstrate that the proposed approach is able to estimate fluid mobility well. Finally, the actual data processing results show that this method has good ability in reservoir prediction and fluid recognition.

Presentation Date: Tuesday, October 16, 2018

Start Time: 8:30:00 AM

Location: 206A (Anaheim Convention Center)

Presentation Type: Oral

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

Pore pressure prediction plays an important role in shale gas exploration and fracking technology. The pore pressure in shale cannot be directly measured but to be inferred by the normal compaction trend, so methods based on the effective stress theory are dedicated to establishing a function between seismic interval velocity and pressure. Among them the mostly used method is Eaton’s equation. However, how to precisely quantify the state of compaction remains unsolved. In this study, the AVO/AVA simultaneous inversion was introduced to estimate P-velocity. According to the exponential relationship between the pore pressure and the ratio of velocities, three different methods including the fitting method, the direct calculation method and the model-based direct calculation method based on the Eaton’s equation were used to estimate shale gas pore pressure, respectively. And then a comparative analysis was performed to see the impact of normal compaction trend on the result. It was found that the horizontal continuity of the model-based direct calculation method was the best. The result shows that the approach of estimating the normal compaction trend impacts the pore pressure significantly.

Presentation Date: Monday, October 15, 2018

Start Time: 1:50:00 PM

Location: 210A (Anaheim Convention Center)

Presentation Type: Oral

Artificial Intelligence, Calculation Method, direct calculation, direct calculation method, equation, interval velocity, inversion, model-based direct calculation method, normal compaction trend, pore pressure, pore pressure prediction, prediction, prediction method, Reservoir Characterization, reservoir geomechanics, seismic inversion, Upstream Oil & Gas

Oilfield Places:

- Oceania > Australia > Western Australia > Carnarvon Basin (0.99)
- Asia > China > Sichuan Province > Sichuan Basin (0.99)

SPE Disciplines:

The basic theory of AVO forward modeling and the energy and amplitude distribution relationship between layers can be described by Zoeppritz equations in exploration geophysics. However, the pragmatic applications of Zoeppritz equations are limited due to their intrinsic nonlinearity. Various Zoeppritz approximations are widely used for Amplitude variation with offset inversion. However, the approximate equations are limited by the assumptions of weak properties contrast and small incident angle. Considering the high contrast situations and the difficulty to invert for six parameters simultaneously, a novel Zoeppritz-based exact PP reflection coefficient is proposed. The novel equation has the same accuracy as the Zoeppritz eqution and reduces the elastic parameters from six to three. A direct inversion method is used and the L1 norm constraint is also considered during the nonlinear inversion process. The model tests and field data examples verify the feasibility of the nonlinear inversion based on a novel exact PP reflection coefficient.

Presentation Date: Tuesday, October 16, 2018

Start Time: 9:20:00 AM

Location: Poster Station 13

Presentation Type: Poster

Bayesian theory is widely used in the prestack inversion. It improves the accuracy and stability of the inversion by adding a priori information in regularization inversion procedure. After a long period of research, Bayesian sparse inversion methods are constantly improving and enter the application research stage. As we all know, the signal is nonstationary in actual seismic processing due to the attenuation. Hyperbolic smoothing method is an effective method to obtain the attenuation function. The addition of nonstationary information to inversion can improve the inversion effect. Then we use these methods in Bayesian sparse inversion for model test and the results show that the new method can acquire higher resolution and stability, thus providing more detail information.

Presentation Date: Thursday, September 28, 2017

Start Time: 10:35 AM

Location: 370D

Presentation Type: ORAL

A direct non-linear AVO inversion method (we can call it inverse operator estimation) and non-linear equation were utilized in this paper. The non-linear inversion is more precise and can be applied to high contrast situations. Considering the effect of parameters sensitivity and the poor non-linear AVO inversion results of impedance and velocity parameters, we adjust the parameters of the non-linear equation, avoid the inaccuracy caused by parameters sensitivity and get the ideal non-linear AVO inversion results of Lamé parameters. The feasibility and stability of the non-linear equation based on Lamé parameters and method are verified by the model and the real data examples.

Presentation Date: Wednesday, September 27, 2017

Start Time: 9:45 AM

Location: Exhibit Hall C/D

Presentation Type: POSTER

algorithm, Artificial Intelligence, AVO inversion, AVO inversion method, equation, inversion, inversion result, Lamé parameter, linear approximation, linear avo inversion, linear equation, linear inversion, machine learning, non, reflectivity, Reservoir Characterization, sensitivity, sin 2, synthetic data, Upstream Oil & Gas

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)

The difficulty of prestack three parameter inversion is the stability of inversion and the uniqueness of the results. In order to solve this problem, there are many ways have been tried, in addition to discard density terms. According to Bayesian probability theory, It is an ideal method to add a priori constraints to reduce the uncertainty of inversion. So that we need to add more prior information such as geological priori information and well log information to reduce the uncertainty. From the seismic exploration theory and practical experience, It is confirmed that the seismic reflection (diffraction) wave field from the subsurface sedimentary strata is characteristic or compressible. Therefore, the parameters of the layered strata model are also compressible or sparse. Utilizing the sparse prior of seismic data, this paper introduces the compressed sensing theory to construct a sensing matrix to reduce the dimensionality of inversion matrix. Then we carry out the reflection coefficient in compressed sensing domain by sparse reconstruction method which named POCS algorithm to ensure the stability of the three parameter inversion. Finally, Synthetic examples and the field seismic data are tested. The result demonstrate that proposed compressed sensing three parameters AVO inversion strategy can improve the stability of elastic parameters estimation and be capable of provide more credible density information.

Presentation Date: Wednesday, September 27, 2017

Start Time: 9:45 AM

Location: 370D

Presentation Type: ORAL

Artificial Intelligence, AVO inversion, avo-c inversion, condition number, density information, elastic parameter, equation, information, inversion, inversion matrix, inversion result, inversion scheme, matrix, null, parameter inversion, reflection coefficient, Reservoir Characterization, seismic data, sparse, stability, Upstream Oil & Gas

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.54)

Summary Seismic inversion works as an important tool to transfer the interface information of seismic data to the formation information, which renders the seismic data easily understood by geologists or petroleum engineers. A robust multi-trace inversion method under Bayesian inversion theory is proposed in this study to enhance the vertical resolution of seismic inversion and overcome the instability of the inversion existing among different traces in conventional trace by trace seismic inversion approach. The Markov process is initially introduced to describe the relationship between each trace and their correlation and it is closed coupled in the target function of multi-trace seismic inversion. A recursive equation is further derived to simply the inversion process by considering the particularity of the coefficient matrix in the multi-trace inversion equation. Finally, model and field data examples demonstrate that both the conventional and multi-trace basis pursuit reflection coefficient inversion methods are helpful in enhancing resolution of thin layers with inversion results and those thin layers are usually difficult to discern in original seismic profile, which show the advantage of those kinds of inversion methods in thin bed interpretation.

Artificial Intelligence, basis Pursuit, basis pursuit inversion, basis pursuit seismic, Bayesian Inference, equation, information, inversion, inversion method, inversion result, machine learning, multi-trace basis, multi-trace basis pursuit, multi-trace inversion, probability, reflection coefficient, Reservoir Characterization, resolution, seismic data, seismic inversion, Upstream Oil & Gas

Technology:

Thank you!