2) Meandering river from Cranfield data
3) Campos Basin Turbidite
Next-Shot@Geomodeling
Because Geology Matters
We model a sea-floor fan from 15 wells (Data from E. Kvale et al.) in a carbonate-siliclastic turbidite environment. The picture above shows that the well's lithofacies data is matched at each location while generating a realistic fan geometry at every deposition step. The generated granulometry trend in the volume provides valuable information to interpolate rock properties information.
The model fits a tightly spaced set of pseudo wells interpreted along the outcrop, including a local erosion and a very heterogeneous deposition profile across the different wells. As shown in the figure above, thin shale layers are preserved. (Outcrop data from S. Rohais et al.).
Given two seismic slices (Data from V. Koala et al.) where the same migrating channel is interpreted, we create the intermediate channel geometries (including the point bars). We also create the reservoir grid shown in the picture. In function of the river migration and deposition rates, we can stochastically generate many models whose connectivity will vary greatly, providing an extensive range of uncertainty on the connected volumes.
Given seven wells (Data from A. Nolting et al.) with interpreted lithofacies and a satellite image providing the reef outline, we model a sequence of carbonate deposition, building reefs, shoals, and dunes to protect a lagoon locally given the primary direction of the waves. We model the erosion to compute the zones affected by the dissolution when the reef is exposed by decreasing sea level.
5) Modeling Meandering Channels With Seismic Control
6) Wave Dominated shoreface and river lagoon deposits
In this example, we incorporate seismic data to refine the geological model. From the seismic interpretation (from C. Bruhn), we add two listric faults and one channel belt into the turbiditic deposition environment. The two listric faults rotate the fault block, creating a mini-deposition basin. The channel belt information constrains the location of the main river. This example shows how our process-based modeling method can use seismic and geological interpretation to build constrained, realistic models. The right images also show the granulometry trends computed from the deposition process. This trend can be used as secondary data in interpolating petrophysical properties.
Given three wells (Data from I.D. Bryant et al.) with interpreted lithofacies and a seismic image providing the boundaries of shoreface deposits, we model a sequence of river lagoon and shoreface deposits. An island barrier separates the lagoon from the wave-dominated shoreline, creating an ensemble of shoreface deposits. The tide influences the orientation of the mouth-bar deposits in the lagoon, as noted in the lithofacies interpretation of "tidally influenced distributary channels".
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4) Delaware Basin WolfCamp A Turbidite
1) Turbidite Lobe
The 3D view shows the reservoir grid modeled inside the 3D model fitting the 11 wells (Data from I. Dawuda). As the cross-section shows, the wells are primarily interpreted to be inside the point bars.
The granulometry shown for each grid cell follows the river profile and can be used as trend information to interpolate the well's derived porosity and permeability values.
The 3D reservoir grid is built inside the 3D model and is shown above. The thin shale layer is preserved as part of the grid geometry or topology.
6) Modeling Carbonate Reefs and Lagoon Deposits
Our method inputs are very simple and can be extracted from publications. This page shows six examples of models created using our C-FSM method.
Below, we show the geological and reservoir models built from the digitized data in various deposition environments (Turbidite, Meandering Rivers, Carbonate Reef, Wave-Dominated Delta).