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Wu, Wenting (University of Wyoming) | Grana, Dario (University of Wyoming) | Campbell-Stone, Erin (University of Wyoming) | McLaughlin, Fred (University of Wyoming)

**Summary**

Facies classification at the well location is generally based on sedimentological models, however, the extension of the classification of an individual well into the entire reservoir model is contingent on the calibration of a rock physics model that links rock and fluid properties with geophysical measurements such as seismic velocities and/or electromagnetic-derived resistivities. The goal of this work is to present a workflow to define a geologically consistent facies classification at the well location, to accurately reconstruct this classification using elastic and electrical properties, and to extend the classification to the 3D reservoir model. The initial classification at the well location is obtained using traditional statistical methods applied to computed rock properties such as mineralogical volumes, porosity, density and permeability. The facies reconstruction based on elastic/electrical properties is obtained using a Bayesian approach that combines rock physics with statistical models. The workflow is illustrated through the application to the Rock Springs Uplift field, Wyoming, which hosts several potential CO2 storage reservoirs.

**Introduction**

Facies classification aims to assign a rock type or class to each location of a 3D reservoir model, based on the available rock and fluid properties. At the well location, the classification can be based on measured well log data, formation-evaluation computed curves, and core samples. However, far away from the well, most of these properties are not available and the classification must be derived from geophysical properties estimated from surface measurements, such as seismic and electromagnetic properties. Several methods for facies classification have been presented in literature (Doyen, 2007; Avseth et al., 2005; MacGregor, 2012). These methods generally differ from the mathematical approach (deterministic or statistical methods) and for the input data (core samples, well logs, or geophysical inverted attributes). The scale of core samples allows petrophysicists to generate a very detailed facies description; however, the extension of this classification to well log and to 3D reservoir models is difficult to achieve due to the lower resolution and data noise of well logs and surface geophysical measurements respectively. The aim of this work is to show that the integration of a rock physics model in the classification and the use of statistical methods for uncertainty quantification can overcome this limitation.

Artificial Intelligence, bayesian facies classification, Bayesian Inference, classification, co 2, cutoff method, dolomite, elastic facies, electrical property, facies, facies classification, facies profile, limestone, log analysis, machine learning, madison formation, permeability, porosity, Reservoir Characterization, rock property, statistical rock physics modeling, Upstream Oil & Gas, well location, well logging, workflow

Oilfield Places:

- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale (0.99)
- North America > United States > Pennsylvania > Appalachian Basin > Marcellus Shale (0.99)
- (9 more...)

SPE Disciplines:

Technology:

- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.47)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.35)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.35)

**Summary**

Estimation of reservoir properties and monitoring of fluid flow is an important modeling component in hydrocarbon exploration and production. Geophysical data (elastic and electrical) and reservoir rock properties can be linked via rock physics models. In this application, we develop a joint inversion approach of P-wave velocity and resistivity well logs for total porosity and total volume of fluid. We applied our inversion workflow to wireline data measured at the well location of the Rock Springs Uplift, Wyoming, USA, a potential CO_{2} storage reservoir. The inversion scheme is applied to the actual well dataset and to the same set of well logs filtered at lower resolutions. Results show that the rock and fluid volumes, or at least their trends in the case of filtered data, were predicted by the inversion scheme. Although the method was applied only at the well location, the inversion workflow can be extended to the entire reservoir model to predict the rock and fluid properties of the reservoir away from the well location.

application, Archie, Artificial Intelligence, dolomite, inversion, joint rock physics inversion, log analysis, machine learning, p-wave velocity, porosity, Reservoir Characterization, resistivity, resolution, rock physics inversion, rock physics model, sandstone, Upstream Oil & Gas, well location, well log data, well logging, workflow

Oilfield Places:

- North America > United States > Wyoming > Sand Wash Basin (0.99)
- North America > United States > Wyoming > Green River Basin (0.99)
- North America > United States > Utah > Sand Wash Basin (0.99)
- (6 more...)

SPE Disciplines:

One of the emerging technologies in geophysics is the stochastic inversion of geophysical data for the prediction of rock and fluid properties. The probability distribution of the geophysical properties of interest can be computed using a probabilistic inverse method. The integration of stochastic inverse methods and geophysical modeling allows generating multiple reservoir models of rock and fluid properties that honor the geophysical measurements. Stochastic approaches allow sampling multiple solutions from the posterior distribution of the model parameters and quantifying the uncertainty in the model parameter predictions. Stochastic inversion algorithms can be applied to seismic inversion problems as well as petrophysical inversion problems. In this work, we discuss analytical and numerical approaches, as well as their advantages and disadvantages.

Presentation Date: Monday, September 25, 2017

Start Time: 1:50 PM

Location: 381A

Presentation Type: ORAL

analytical method, annual meeting, application, Artificial Intelligence, Forward Model, geophysics, inverse method, inverse problem, inversion, linearization, machine learning, model parameter, omre, posterior distribution, prediction, realization, Reservoir Characterization, seg seg international exposition, seismic data, seismic inversion, stochastic inversion, Upstream Oil & Gas

Oilfield Places:

- Europe > United Kingdom > North Sea Basin (0.98)
- Europe > Norway > North Sea Basin (0.98)
- Europe > Netherlands > North Sea Basin (0.98)
- Europe > Denmark > North Sea Basin (0.98)

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

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.

application, Artificial Intelligence, bayesian gaussian mixture, bayesian linear inverse problem, estimation, facies, Gaussian distribution, Gaussian mixture, gaussian mixture inversion, gaussian mixture linear inversion, geophysics, inversion, linear inverse problem, machine learning, realization, Reservoir Characterization, reservoir property, seismic data, sequential approach, Upstream Oil & Gas

Country:

Oilfield Places:

- Europe > United Kingdom > North Sea Basin (0.98)
- Europe > Norway > North Sea Basin (0.98)
- Europe > Netherlands > North Sea Basin (0.98)
- Europe > Denmark > North Sea Basin (0.98)

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

Technology:

**Summary**

Traditionally static reservoir models are obtained as a solution of an inverse problem where reservoir properties, such as porosity and lithology, are estimated from seismic data. With the emergence of time-lapse reservoir models, we can integrate static and dynamic reservoir properties in the seismic reservoir characterization workflow. Here, we propose a methodology to jointly estimate rock properties, such as porosity, and dynamic property changes, such as pressure and saturation changes, from time-lapse seismic data. This methodology is based on a full Bayesian approach to seismic inversion and can be divided into two steps. First we estimate the conditional probability of elastic properties and their relative changes, then we estimate the posterior probability of static rock properties and dynamic property changes. We applied the proposed methodology to a synthetic reservoir study where we created a synthetic seismic survey for a real dynamic reservoir model including pre-production and production scenarios. The final result is then a set of point-wise probability distributions that allow us to predict the most probable reservoir models at each time step and to evaluate the associated uncertainty.

Artificial Intelligence, Bayesian Inference, dynamic property change, estimation, geophysics, inversion, machine learning, porosity, posterior distribution, pressure change, probability, relation, relative change, Reservoir Characterization, reservoir geomechanics, reservoir model, reservoir property, rock physics model, rock property, saturation change, seg houston 2013, seismic data, seismic inversion, time-lapse seismic data, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Seismic (four dimensional) monitoring (1.00)

Thank you!