We replace the traditional correlation+facies modeling steps with a constrained, process-based, depositional process model to define realistic geobodies fitting the well's data. The body's boundaries will define the reservoir grid geometry and fine-scale correlations.
The grid is built after the sedimentary modeling, not a priori, as in the traditional workflow. The thin shale layers are preserved in the grid geometry or topology as transmissivity barriers. The modeled sedimentary body's intrinsic properties will provide trend information for interpolating or simulating rock properties.
Integrating geology, seismic, and production
Description
A revolutionary technology
Comparing with other modeling methods
How does it work?
C-FSM integrates three different kinds of information to construct the geological model. It uses wells' lithofacies and the conceptual model to define the paleo-geography and the deposition environments.
C-FSM uses seismic information in multiple ways, such as constraining the paleo-landscape with information about valleys, mini-basins, syn-sedimentary faults, and lithofacies proportions.
C-FSM uses production information to control the geobodies' organization and shapes away from the wells.
Our method is rule-based, as defined in the geologic modeling continuum by Pyrcz et al. (2015). It includes all the techniques of other rule-based methods (object-based, surface-based, process-based) but focuses on controlling the depositional processes and objects per depositional environment. Another critical aspect is that our method is multi-scale both in time and space. This allows the integration of basin information at the wells' scale.
Compared with traditional FSM, our method relies on the wells' lithofacies intervals to control the deposition landscape, conditions, and quantities. Traditional FSM is parameterized with time and transport, and C-FSM is parameterized with what is observed in the deposition record, back-translating this information into time and transport.
What do we not need? We do not need variograms, complex parameters typically required by other processed-based systems (sediment input, diffusion coefficient, grain density, etc.), or detailed markers' correlation.
What do we need: the depositional environment and some of its geometrical constraints, the range of dimensions of the deposition bodies, the lithofacies interpretation (lithology & depositional setting, see SEPM definition), and the eustatic sea-level curve correlated to the parasequences (when applicable).
Traditional facies modeling can be quite complex and, therefore, time-consuming. It requires variograms per facies and facies trends. Facies trends need to be drawn by hand. If you want to use MPS, you must build training images, which can be quite complex. The other issue with MPS is that images " hardcode" the facies juxtapositions and proportions, which may not be valid over the entire grid.
Traditional modeling requires manually building the layering, which requires creating fine-scale correlations. Correlating can be very time-consuming and increase the number of layers to be modeled in the grid, adding complexity to the process.
Accelerating modeling
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Constrained-Forward Stratigraphic Modeling
Simple to use
Next-Shot@Geomodeling
Because Geology Matters
Why?
C-FSM
Today, building reservoir models is a three-step process: first, correlate well picks; second, build a stratigraphic grid; and third, use geostatistics to compute facies and property values.
Different problems exist with this method:
- The correlation of markers across many wells is sometimes complicated.
- Creating the grid to honor the depositional model can be very challenging.
- With today's technology, creating realistic facies models is laborious. Facies models may need trend maps and/or training images.
- The size of the grid cells leads to the loss of thin shale layers upscaled into larger cells.
- Pixel-based modeling cannot guarantee the continuity of flow barriers, and variogram-based models do not capture the natural spatial heterogeneity. All these can lead to an overstatement of permeability.
Input
In input, we take wells' lithofacies interpretation. From seismic, we use geomorphology constraints (like valley geometry and faults) and lithofacies proportions constraints.
From production data, we interpret flow barriers from pressure tests and connected volume indicators from production. Flow barriers are associated with deposition body constraints. Connected volume information is associated with facies' spatial organization. For example, higher connectivity can be related to increased meandering, or loss of connectivity between two wells can be associated with increased sinuosity between the two wells.
Constrained Simulation
We stochastically construct a deposition sequence using forward stratigraphic modeling. The interpreted lithofacies intervals control each sedimentary body's position and thickness. If seismic information is available, it can constrain the paleo-landscape or the terminal shapes of a meandering river. If production information is available, it will constrain the shape away from the wells.
Output
We use all bodies' boundaries to construct a reservoir grid preserving thin-shale layers.
We provide deposition trends to use when simulating grid porosity and permeability.