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to

GoCharacterizing thin shaly-sand reservoirs using seismic data can be challenging due to limited seismic resolution. This paper investigates seismic signatures of thin shaly-sand reservoirs with statistical attributes extracted using kernel principal component analysis (KPCA). We model sand-shale sequences as a first-order, discrete, 1-D Markov chain. Then we assign properties to the sand-shale layers using established rock-physics relations. From multiple realizations of synthetic seismograms, we extract statistical attributes using KPCA with Gaussian and polynomial kernels. Results show that these attributes can be used to differentiate sequences with different net-to-gross ratios or different water saturations. Thus a workflow similar to our synthetic study using KPCA can potentially be applied to real seismic data to characterize thin shaly-sand reservoirs.

Artificial Intelligence, classification, kernel principal, KPCA, layer, lithologic state, machine learning, matrix, model, polynomial, principal component analysis, property, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, sequence, Signature, Stanford University, thin shaly-sand reservoir, transition matrix, 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)

Time lapse seismic modeling is an important step in joint inversion of time lapse seismic and production data of a field. Rock physics analysis is the basis for modeling the time-lapse seismic. However joint inversion of both types of data for estimation of reservoir parameters is highly non-linear and complex with uncertainties at each step of the process. So it is essential, before proceeding with large scale history matching, to investigate sensitive rock physics parameters in modeling time lapse seismic signature of a field. The rock physics analysis includes facies classification based on the well log data and generation of initial 3D seismic velocity-porosity cube. Then Gassmann's equation is used to generate time-lapse seismic by taking in to account the changes in the saturation and pressure of the reservoir. In this study we have used the data set of Norne field. At first facies are classified based on the well log data. Then we investigate sensitive parameters in the Gassmann's equation to generate the initial seismic velocities. The investigated parameters include mineral properties, water salinity, pore-pressure and gas-oil ratio (GOR). Next we investigate parameter sensitivity for time-lapse seismic modeling of Norne field. The investigated rock physics parameters are clay content, cement, pore-pressure and mixing. This sensitivity analysis helps to select important parameters for seismic history matching.

change, fluid substitution, Gassmann, lapse seismic, model, modeling, modeling time-lapse seismic, Norne field, production, Reservoir Characterization, reservoir description and dynamics, rock physics, saturation, seismic processing and interpretation, sensitivity, Signature, study, Upstream Oil & Gas, variation, well, well log

Oilfield Places:

- Europe > Norway > Norwegian Sea > Halten Bank Area > Norne Oil Field (0.99)
- Europe > Norway > Norwegian Sea > Ile Formation (0.94)

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

Seismic reservoir characterization aims to transform obtained seismic signatures into reservoir properties such as lithofacies and pore fluids. We propose a Markov chain Monte Carlo (McMC) workflow consistent with geology, well-logs, seismic data and rock-physics information. The workflow uses a multiple-point geostatistical method for generating realizations from the prior distribution and Adaptive Spatial Resampling (ASR) for sampling from the posterior distribution conditioned to seismic data. Sampling is a general approach for assessing important uncertainties. However, rejection sampling requires a large number of evaluations of forward model, and is not efficient for reservoir modeling. Metropolis sampling is able to perform a reasonably equivalent sampling by forming a Markov chain. The ASR algorithm perturbs realizations of a spatially dependent variable while preserving its spatial structure. The method is used as a transition kernel to produce a Markov chain of geostatistical realizations. These realizations are converted to predicted seismic data by forward modeling, to compute the likelihood. Depending on the acceptation/rejection criterion in the Markov process, it is possible to obtain a chain of realizations aimed either at characterizing the posterior distribution with Metropolis sampling or at calibrating a single realization until an optimum is reached. Thus the algorithm can be tuned to work either as an optimizer or as a sampler. The validity and applicability of the proposed method and sensitivity of different parameters is explored using synthetic seismic data.

Artificial Intelligence, Bayesian Inference, cti, distribution, ect, geologic modeling, geological modeling, machine learning, Markov chain, Markov chain Monte Carlo, PLI, posterior, Reservoir Characterization, reservoir description and dynamics, SAM, seismic processing and interpretation, stochastic seismic reservoir characterization, TiO, Upstream Oil & Gas

SPE Disciplines:

Technology:

Using volume averaging we generalize Gassmannâ€™s (1951) isotropic equation for fluid-filled porous media to solid-filled porous media with disconnected pores. This exact equation can be used as an analog of Gassmann's fluid substitution transform for solid-filled porous media, since it predicts the change in effective moduli upon solid substitution, depending

approximate solid substitution, approximate solid substitution transform, approximation, bulk modulus, change, Eqn, equation, flow in porous media, Fluid Dynamics, geometry, necessary condition, pore, pore geometry, porosity, Reservoir Characterization, reservoir description and dynamics, shear, shear modulus, solid substitution, stiffness, stress, transform, Upstream Oil & Gas

SPE Disciplines:

We propose here a methodology to sequentially simulate elastic and reservoir properties, such as impedances, porosity, and facies, in order to obtain a set of reservoir models conditioned by seismic data. The sequential inversion approach provides multiple simulations obtained as solution of a Bayesian linear inverse problem where we assume that rock properties are distributed according to a Gaussian mixture model, i.e. a linear combination of Gaussian distributions. The weights of the Gaussian components are the probabilities of the facies. The main advantage of this approach is that the assumption of Gaussian mixture distribution of reservoir properties overcomes the common Gaussian assumption allowing the description of the multimodal behavior of the data. Furthermore the solution of the Bayesian Gaussian mixture linear inverse problem preserves analytical tractability. The linear inverse theory results valid in the Gaussian case have been extended to the multimodal case, by deriving the analytical expression of means, covariance matrices, and weights of the Gaussian mixture conditional distribution of the model. We describe here how to compute the analytical solution and implement a sequential approach in the simulation algorithm. We then apply the sequential Gaussian mixture linearized inversion to layer maps extracted from a 3D geophysical model of a clastic reservoir located in the North Sea. Impedances are first inverted from seismic data, then porosity and facies are simulated from impedance maps. This application provides a complete set of reservoir models with multimodal rock properties.

Artificial Intelligence, Bayesian, distribution, facies, Gaussian, Gaussian mixture, gaussian mixture linear, inverse, inverse problem, inversion, machine learning, methodology, mixture, mixture inversion, model, porosity, posterior, problem, realization, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, sequential, Upstream Oil & Gas

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

Dynamic data, including production and time lapse seismic can be used in history matching process for better description of the reservoir and thus for better reservoir forecasting. However joint inversion of time lapse seismic and production data is complex and challenging with uncertainties at each step of the process. So it is essential, before proceeding with large scale history matching, to investigate parameter sensitivity for both types of data. In this study the data set of Norne field is used to find out which reservoir rock and fluid parameters have the most impact on time lapse seismic and production data at this field. The result of this study will be used in history matching of time lapse seismic and production data of Norne field.

doi: 10.2118/153039-MS

SPE-153039-MS

change, compressibility, flow in porous media, Fluid Dynamics, formation evaluation, History, joint inversion, lapse seismic, model, Norne field, pore compressibility, porosity, production, Reservoir Characterization, reservoir description and dynamics, rock, rock physics model, seismic processing and interpretation, sensitivity, society of petroleum engineers, study, Upstream Oil & Gas, variation, well

Oilfield Places:

- Europe > Norway > Norwegian Sea > Halten Bank Area > Norne Oil Field (0.99)
- Europe > Norway > Norwegian Sea > Ile Formation (0.94)

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Seismic (four dimensional) monitoring (1.00)

Thank you!