**Source**

**Conference**

**Theme**

**Author**

- Cheng, Guangsen (1)
- Du, Jiayuan (1)
- Du, Zeyuan (1)
- Guangsen, Cheng (1)
- Guo, Junchao (1)
- He, Dongyang (1)
- Hongxue, Zhang (1)
- Lei, Ting (1)
- Li, Kun (11)
- Liu, Daoli (1)
- Liu, Hanqing (1)
- Lu, Li (1)
- Wei, Guohua (1)
- Wu, Guochen (5)
- Xi, Yijun (1)
- Yang, Hongwei (1)
- Yin, Xingyao (23)
- Zhao, Maoqiang (1)
- Zhao, Zhengyang (1)
- Zheng, Ying (2)
- Zhou, Dongyong (1)
- Zhou, Qichao (1)
- Zhu, Ming (1)
**Zong, Zhaoyun (27)**

**Concept Tag**

- absorption parameter (1)
- aki-richard approximate equation (1)
- algorithm (1)
- application (1)
- approximation (2)
- Artificial Intelligence (20)
- asymptotic analysis (1)
- atom (1)
- attenuation (3)
- AVA inversion (1)
- ava-tf inversion (1)
- AVAZ inversion (1)
- AVO (1)
- AVO gradient (1)
- AVO inversion (6)
- avo inversion approach (1)
- AVO inversion method (1)
- azimuth (1)
- basis Pursuit (1)
- basis pursuit inversion (1)
- basis pursuit seismic (1)
- Bayesian (2)
- Bayesian Inference (7)
- Bayesian inversion (3)
- boundary (1)
- broadband seismic (1)
- bulk modulus (1)
- calculation (1)
- Calculation Method (1)
- Cauchy (1)
- Cauchy distribution (1)
- cdp number time (1)
- coefficient (2)
- complex frequency domain (2)
- constraint (5)
- conventional avo gradient (1)
- digital core (2)
- elastic impedance (3)
- elastic impedance inversion (2)
- elastic parameter (4)
- equation (14)
- estimation (7)
- finite element (2)
- Fluid Dynamics (1)
- fluid factor (2)
- fracture (2)
- Fracture Orientation Estimation (1)
- frequency (10)
- frequency component (2)
- frequency dependent (2)
- frequency domain (2)
- Gaussian (3)
- Gaussian distribution (1)
- Gaussian mixture (1)
- impedance (5)
- incident angle (3)
- Indicator (1)
- information (6)
- initial model (2)
- inversion (23)
- inversion method (5)
- inversion result (11)
- Lamé parameter (1)
- linear inversion (2)
- low frequency (3)
- low frequency component (2)
- machine learning (10)
- media (2)
- model parameter (3)
- noise (2)
- Nonlinear Inversion (1)
- null (2)
- null null (2)
- null null null (2)
- Overthrust model (1)
- permeability (2)
- Poisson ratio (1)
- probability (4)
- quality factor (2)
- reflection (3)
- reflection coefficient (5)
- reflectivity (5)
- Reliability enhancement (1)
- Reservoir Characterization (27)
- reservoir fluid (2)
- resolution (4)
- rock physics (2)
- s-wave velocity (3)
- SEG (1)
- SEG SEG San Antonio (1)
- segseg houston 2013 (2)
- seismic data (8)
- seismic inversion (5)
- seismic wave (2)
- Simulation (2)
- Thickness (1)
- time domain (3)
- Upstream Oil & Gas (27)
- Velocity Dispersion (1)
- Zoeppritz equation (1)

**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:

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:

Yang, Hongwei (Geophysical Research Institute, Shengli Oilfield Branch, Company of Sinopec, Dongying, China University of Petroleum-East China) | Wei, Guohua (Geophysical Research Institute, Shengli Oilfield Branch, Company of Sinopec, Dongying, China University of Petroleum-East China) | Zhao, Maoqiang (Geophysical Research Institute, Shengli Oilfield Branch, Company of Sinopec, Dongying, China University of Petroleum-East China) | Xi, Yijun (Geophysical Research Institute, Shengli Oilfield Branch, Company of Sinopec, Dongying, China University of Petroleum-East China) | Zong, Zhaoyun (Geophysical Research Institute, Shengli Oilfield Branch, Company of Sinopec, Dongying, China University of Petroleum-East China)

The attenuation of seismic wave is well related to the oil and gas in the reservoirs. It can be quantitatively characterized by the value of quality factor Q. The pre-stack elastic impedance inversion contains more information compared to the post-stack data, but also can obtain the formation quality factor that reflects the absorption and attenuation of the subsurface medium. Based on the Q elastic impedance equation, this study establishes the quantitative relationship between the elastic impedance and the absorption parameter. Combined with the rock physics analysis, the elastic parameter and absorption parameter of the reservoir are obtained by the pre-stack four parameters inversion algorithm. The parameters correspond to the P-wave velocity, S-wave velocity, density, and absorption parameter related to the quality factor Q, respectively. The application of real seismic data indicates that this method has more advantages in reservoir identification compared to the conventional three parameters inversion.

Presentation Date: Monday, October 15, 2018

Start Time: 1:50:00 PM

Location: 209A (Anaheim Convention Center)

Presentation Type: Oral

absorption parameter, attenuation, coefficient, elastic impedance, elastic impedance inversion, elastic parameter, fluid discrimination, impedance, inversion, inversion result, p-wave velocity, quality factor, reflection, Reservoir Characterization, s-wave velocity, seismic data, seismic wave, Upstream Oil & Gas

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

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

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)

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:

Low frequencies missing from conventional seismic data are conventionally obtained from other geophysical information sources, such as well log data, for estimating absolute rock properties, which results in biased inversion results for complex heterogeneous geological targets or plays with sparse well log data, such as marine or deep stratum. Broadband seismic data lead to new opportunities to estimate the low frequency of elastic parameters without well log data. A novel AVO inversion approach with a Bayesian inference for broadband seismic data is proposed in this study. The low frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with a Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is able to recover the long-wavelength structures of the elastic model. Furthermore, with those low frequency components as initial models and constraints, the conventional AVO inversion with a Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and real data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to estimate the low frequency components of elastic parameters well.

Presentation Date: Tuesday, September 26, 2017

Start Time: 10:10 AM

Location: Exhibit Hall C/D

Presentation Type: POSTER

Artificial Intelligence, AVO inversion, avo inversion approach, broadband seismic, complex frequency domain, elastic parameter, frequency, inversion, inversion result, low frequency, low frequency component, null, null null, null null null, Reservoir Characterization, seismic data, time domain, Upstream Oil & Gas

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.76)

Local fluid flow is a main cause of wave attenuation and velocity dispersion when seismic waves propagate in the porous media. Many authors have studied the local fluid flow process and derived the wave equations in the model for gas pockets in a water-saturated porous medium. However, the wave equations in the layered porous model have not been obtained yet. In this study, we establish a layered double-porosity model saturated with a single fluid and derive the wave equations based on Biot’s theory and on a generalization of Rayleigh’s theory. We obtain the phase velocity and quality factor as a function of frequency by plane wave analysis and analyze the characteristics of wave attenuation and velocity dispersion in the layered double-porosity medium. The results suggest that there are three wave modes named as the fast P wave and two slow P waves in porous medium when P wave propagates through elementary volume perpendicularly. The attenuation and the velocity dispersion of seismic waves in the low frequency range illustrate the mesoscopic loss mechanism due to local fluid flow.

Presentation Date: Monday, September 25, 2017

Start Time: 4:45 PM

Location: 351D

Presentation Type: ORAL

Artificial Intelligence, attenuation, dissipation factor, double-porosity media, double-porosity model, equation, flow in porous media, Fluid Dynamics, fluid flow, frequency, kinetic energy, layered double-porosity media, low frequency, permeability, phase velocity, Reservoir Characterization, Thickness, upper layer, Upstream Oil & Gas, Velocity Dispersion, wave attenuation, wave propagate

SPE Disciplines:

Thank you!