Bayesian Hamiltonian Monte Carlo method for the estimation of pyrolysis parameter S1

Geophysics ◽  
2021 ◽  
Vol 86 (6) ◽  
pp. M197-M209
Author(s):  
Kun Luo ◽  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Hong Cao ◽  
Minghui Lu
Keyword(s):  
Monte Carlo ◽  
Seismic Data ◽  
Rock Physics ◽  
Gaussian Mixture ◽  
Organic Carbon Content ◽  
Shale Oil ◽  
Important Indicator ◽  
Hamiltonian Monte Carlo ◽  
Petrophysical Parameters ◽  
Sweet Spots

A Gaussian mixture Hamiltonian Monte Carlo (HMC) Bayesian method has been developed for the inversion of petrophysical parameters such as pyrolysis parameter S1, which is driven by a statistical shale rock-physics model. Pyrolysis parameter S1 can be used to indicate the content of free or adsorbed hydrocarbons in source rock, and it is an important indicator to evaluate the production of shale oil reservoirs. However, most studies on pyrolysis parameters are based on pyrolysis experiments and there is no relevant study to inverse pyrolysis parameter S1 from seismic data. In addition, compared to the total organic carbon content, pyrolysis S1 is more accurate for evaluating gas and oil in shale. In particular, high values of pyrolysis S1 can directly indicate the content of shale oil. We have developed a strategy for assessing shale oil sweet spots through estimating pyrolysis S1 and other petrophysical parameters. Based on the Gaussian mixture assumptions for the prior distribution of the model, we build a joint distribution to link the pyrolysis parameter S1 with elastic attributes, and then we derive a formulation to inverse S1 with the Bayesian model. Due to the components of the Gaussian mixture, the HMC method has been used to sample the posterior distribution. Our study finds that the HMC method for sampling can improve the efficiency and allow a more robust quantification of the uncertainty; also, application to real seismic data sets indicates that the delineation of sweet spots is more accurate combined with pyrolysis S1.

Seismic reservoir characterization of Utica-Point Pleasant Shale with efforts at quantitative interpretation — A case study: Part 1

2018 ◽  
Vol 6 (2) ◽  
pp. T313-T324 ◽  
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Hossein Nemati ◽  
James Keay
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Oil And Gas Industry ◽  
Source Rocks ◽  
Organic Carbon Content ◽  
Eastern United States ◽  
Neural Network Approach ◽  
Simultaneous Inversion ◽  
Organic Porosity ◽  
Sweet Spots

The Utica Shale is one of the major source rocks in Ohio, and it extends across much of the eastern United States. Its organic richness, high content of calcite, and development of extensive organic porosity make it a perfect unconventional play, and it has gained the attention of the oil and gas industry. The primary target zone in the Utica Play includes the Utica Formation, Point Pleasant Formation, and Trenton Formation intervals. We attempt to identify the sweet spots within the Point Pleasant interval using 3D seismic data, available well data, and other relevant data. This has been done by way of organic richness and brittleness estimation in the rock intervals. The organic richness is determined by weight % of total organic carbon content, which is derived by transforming the inverted density volume. Core-log petrophysical modeling provides the necessary relationship for doing so. The brittleness is derived using rock-physics parameters such as the Young’s modulus and Poisson’s ratio. Deterministic simultaneous inversion along with a neural network approach are followed to compute the rock-physics parameters and density using seismic data. The correlation of sweet spots identified based on the seismic data with the available production data emphasizes the significance of integration of seismic data with all other relevant data.

scholarly journals Prediction of sweet spots in shale reservoir based on geophysical well logging and 3D seismic data: A case study of Lower Silurian Longmaxi Formation in W4 block, Sichuan Basin, China

2016 ◽  
Vol 35 (2) ◽  
pp. 147-171 ◽  
Author(s):  
Sheng Chen ◽  
Wenzhi Zhao ◽  
Yonglin Ouyang ◽  
Qingcai Zeng ◽  
Qing Yang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Gas Content ◽  
3D Seismic ◽  
Elastic Parameters ◽  
Longmaxi Formation ◽  
Shale Reservoir ◽  
3D Seismic Data ◽  
Evaluation Parameters ◽  
Sweet Spots

W4 block of Sichuan Basin is a pioneer in shale gas exploration and development in China. But geophysical prospecting is just at its beginning and thus has not provided enough information about how sweet spots distribute for the deployment of horizontal well. This paper predicted sweet spots based on logging and 3D seismic data. Well logging interpretation method was used to get the key evaluation parameters of shale reservoir and determine the distribution of sweet spots in vertical direction. Rock physics analysis technology was used to define the elastic parameters that were sensitive to the key evaluation parameters, such as TOC and gas content of shale gas reservoir. At the same time the quantitative relationships between them were established. Based on the result of seismic rock physics analysis, prestack inversion was carried out to predict the transverse plane distribution of the key evaluation parameters of shale reservoir. These research results are integrated to determine the distribution of sweet spots. The results show that sweet spots in this area were characterized by high TOC content, high gas content, high GR, high Young’s modulus, low Poisson’s ratio, low density, and low P-wave velocity. Density was the most sensitive elastic parameters to TOC of the reservoir. The optimal combination for predicting the gas content is composed of six parameters include density, Poisson’s ratio, and so on. Sweet spots in this block vertically concentrate within 30 m above the bottom of Longmaxi Formation. Two classes of sweet spots have been predicted in this area, class I sweet spots are recommended to be prioritized for development. This study effectively predicted the spatial distribution of sweet spots, which provide important guidance for the development of the area.

Seismic reservoir mapping from 3‐D AVO in a North Sea turbidite system

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 1157-1176 ◽  
Author(s):  
P. Avseth ◽  
T. Mukerji ◽  
A. Jørstad ◽  
G. Mavko ◽  
T. Veggeland
Keyword(s):  
Monte Carlo ◽  
North Sea ◽  
Seismic Data ◽  
Rock Physics ◽  
Well Log ◽  
Pore Fluids ◽  
Seismic Properties ◽  
Feeder Channel ◽  
Turbidite System ◽  
Seismic Amplitudes

We present a methodology for estimating uncertainties and mapping probabilities of occurrence of different lithofacies and pore fluids from seismic amplitudes, and apply it to a North Sea turbidite system. The methodology combines well log facies analysis, statistical rock physics, and prestack seismic inversion. The probability maps can be used as input data in exploration risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic‐scale sedimentary units which we refer to as seismic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick‐bedded clean sands with water, plane laminated thick‐bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the [Formula: see text] ratio helps resolve these ambiguities. We establish a statistically representative training database by identifying seismic lithofacies from thin sections, cores, and well log data for a type well. This procedure is guided by diagnostic rock physics modeling. Based on the training data, we perform multivariate classification of data from other wells in the area. From the classification results, we can create cumulative distribution functions of seismic properties for each facies. Pore fluid variations are accounted for by applying the Biot‐Gassmann theory. Next, we conduct amplitude‐variation‐with‐offset (AVO) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO responses related to the inherent natural variability of each seismic lithofacies using a Monte Carlo technique. Based on the Monte Carlo simulation, we generate bivariate probability density functions (pdfs) of zero‐offset reflectivity [R(0)] versus AVO gradient (G) for different facies combinations. By combining R(0) and G values estimated from 2‐D and 3‐D seismic data with the bivariate pdfs estimated from well logs, we use both discriminant analysis and Bayesian classification to predict lithofacies and pore fluids from seismic amplitudes. The final results are spatial maps of the most likely facies and pore fluids, and their occurrence probabilities. These maps show that the studied turbidite system is a point‐sourced submarine fan in which thick‐bedded clean sands are present in the feeder‐channel and in the lobe channels, interbedded sands and shales in marginal areas of the system, and shales outside the margins of the turbidite fan. Oil is most likely present in the central lobe channel and in parts of the feeder channel.

Joint seismic and petrophysical nonlinear inversion with Gaussian mixture-based adaptive regularization

Geophysics ◽  
2021 ◽  
pp. 1-57
Author(s):  
Qiang Guo ◽  
Jing Ba ◽  
Li-Yun Fu ◽  
Cong Luo
Keyword(s):  
Inverse Problem ◽  
Seismic Data ◽  
Simulated Annealing Algorithm ◽  
Gaussian Mixture ◽  
Forward Model ◽  
Inversion Method ◽  
Model Parameters ◽  
Adaptive Regularization ◽  
Petrophysical Parameters ◽  
Highly Nonlinear

The estimation of reservoir parameters from seismic observations is one of the main objectives in reservoir characterization. However, the forward model relating petrophysical properties of rocks to observed seismic data is highly nonlinear, and solving the relevant inverse problem is a challenging task. We present a novel inversion method for jointly estimating elastic and petrophysical parameters of rocks from prestack seismic data. We combine a full rock-physics model and the exact Zoeppritz equation as the forward model. To overcome the ill-conditioning of the inverse problem and address the complex prior distribution of model parameters given lithofacies variations, we introduce a regularization term based on the prior Gaussian mixture model under Bayesian framework. The objective function is optimized by the fast simulated annealing algorithm, during which the Gaussian mixture-based regularization terms are adaptively and iteratively adjusted by the maximum likelihood estimator, allowing the posterior distribution to be more consistent with the observed seismic data. The adaptive regularization method improves the accuracy of petrophysical parameters compared to the sequential inversion and non-adaptive regularization methods, and the inversion result can be used for indicating gas-saturated areas when applied to field data.

Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. R463-R476 ◽  
Author(s):  
Leandro Passos de Figueiredo ◽  
Dario Grana ◽  
Mauro Roisenberg ◽  
Bruno B. Rodrigues
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Elastic Properties ◽  
Posterior Distribution ◽  
Seismic Data ◽  
Gaussian Mixture ◽  
Gaussian Component ◽  
Well Test ◽  
Mcmc Method

We have developed a Markov chain Monte Carlo (MCMC) method for joint inversion of seismic data for the prediction of facies and elastic properties. The solution of the inverse problem is defined by the Bayesian posterior distribution of the properties of interest. The prior distribution is a Gaussian mixture model, and each component is associated to a potential configuration of the facies sequence along the seismic trace. The low frequency is incorporated by using facies-dependent depositional trend models for the prior means of the elastic properties in each facies. The posterior distribution is also a Gaussian mixture, for which the Gaussian component can be analytically computed. However, due to the high number of components of the mixture, i.e., the large number of facies configurations, the computation of the full posterior distribution is impractical. Our Gaussian mixture MCMC method allows for the calculation of the full posterior distribution by sampling the facies configurations according to the acceptance/rejection probability. The novelty of the method is the use of an MCMC framework with multimodal distributions for the description of the model properties and the facies along the entire seismic trace. Our method is tested on synthetic seismic data, applied to real seismic data, and validated using a well test.

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