Abstract: A computer-based method of conditioning reservoir model data includes performing a modeling process within a 3D stratigraphic grid to generate an initial model including one or more facies objects within the model volume, the modeling process including parametric distributions, initial and boundary conditions as well as depositional and erosional events to define the facies objects within the model volume. The mismatch between this initial model and the conditioning well data and potential input trend model is applied to compute a locally variable constraint model. The method further includes executing a multiple point statistics simulation with this constraint model that varies between completely constrained by the initial model at locations where the initial model is consistent with known well data and potential input trend models, and unconstrained by the initial model at locations where the initial model does not match known well data or potential input trend models to allow conformance to the known data.

Abstract: A computer-based method of conditioning reservoir model data includes performing a modeling process within a 3D stratigraphic grid to generate an initial model including one or more facies objects within the model volume, the modeling process including parametric distributions, initial and boundary conditions as well as depositional and erosional events to define the facies objects within the model volume. The mismatch between this initial model and the conditioning well data and potential input trend model is applied to compute a locally variable constraint model. The method further includes executing a multiple point statistics simulation with this constraint model that varies between completely constrained by the initial model at locations where the initial model is consistent with known well data and potential input trend models, and unconstrained by the initial model at locations where the initial model does not match known well data or potential input trend models to allow conformance to the known data.

Abstract: Methods and systems for trend modeling of subsurface properties are disclosed. One method includes defining a stratigraphic grid of a subsurface volume, the stratigraphic grid including a plurality of columns and a plurality of layers. The method further includes determining, for each layer or column, an initial average property value based at least in part on well data in the subsurface volume and a confidence interval around that initial average property value defining a range of likely values for a target average property value. The method also includes receiving one or more user-defined edits to the initial average property value in one or more of the layers or columns, the one or more edits resulting in the modeled target average property value, and determining whether the modeled target average property value falls within the confidence interval.

Abstract: A computer-implemented method and system for simulating reservoir property trends, including petrophysical trends within facies geobodies, is provided. One embodiment includes discretizing a range of values of the reservoir property to be modeled into at least two categories and utilizing the categories to discretize the associated well data and training image into categorical well data and a categorical training image. An embodiment includes applying MPS simulation using the categorical training image on a stratigraphic grid of nodes discretizing the reservoir volume to create a categorical property model. The embodiment additionally includes applying variogram-based simulation in each sub-region of the categorical property model to create a reservoir property model that reproduces the category histogram of each associated sub-region and models the reservoir property trends.

Abstract: A computer-implemented method and system for simulating reservoir property trends, including petrophysical trends within facies geobodies, is provided. One embodiment includes discretizing a range of values of the reservoir property to be modeled into at least two categories and utilizing the categories to discretize the associated well data and training image into categorical well data and a categorical training image. An embodiment includes applying MPS simulation using the categorical training image on a stratigraphic grid of nodes discretizing the reservoir volume to create a categorical property model. The embodiment additionally includes applying variogram-based simulation in each sub-region of the categorical property model to create a reservoir property model that reproduces the category histogram of each associated sub-region and models the reservoir property trends.

Abstract: An enhanced multi-point statistical (MPS) simulation is disclosed. A multiple-grid simulation approach is used which has been modified from a conventional MPS approach to decrease the size of a data search template, saving a significant amount of memory and cpu-time during the simulation. Features used to decrease the size of the data search template include: (1) using intermediary sub-grids in the multiple-grid simulation approach, and (2) selecting a data template that is preferentially constituted by previously simulated nodes. The combination of these features allows saving a significant amount of memory and cpu-time over previous MPS algorithms, yet ensures that large-scale training structures are captured and exported to the simulation exercise.

Abstract: A method for creating a reservoir facies model is disclosed. A S-grid is created which is representative of a subterranean region to be modeled. A training image is constructed which includes a number of facies. The training image captures facies geometry, associations and heterogeneity among the facies. A facies probability cube corresponding to the S-grid is derived from a geological interpretation of the facies distribution within the subterranean region. Finally, a geostatistical simulation, preferably a multiple-point simulation, is performed to create a reservoir facies model which utilizes the training image and facies probability cube and is conditioned to subsurface data and information. Ideally, the facies probability cube is created using an areal depocenter map of the facies which identifies probable locations of facies within the S-grid.