Well selection is a Kraken’s competence which enables analyzing wells’ neighborhood. In terms of post-processing analysis, this tool permits evaluating grid properties’ distribution within the wells region. It can be very useful to correlate the events occurring among them. The recovery factor and well lifetime are strongly affected by reservoir properties, such as porosity, permeability, and residual oil saturation. Thus, an efficient data visualization which allows the understanding of what happens in a well surround is determinant for a successful development strategy. In the example, a well selection evinces oil saturation distribution through the selected areas.
“IJK Block” is a Kraken capability that allows the user to select the desired range of coordinates in the Main Grid. It consists of a convenient tool to analyze and compare different regions separately. The example provides a visualization of oil volume distribution through the first layer of the grid, using the featured process. An oil quantity analysis can be very useful to identify potential hydrocarbon spots, hence the integrated team can predict suitable regions to drill appraisal wells. Also, the lower concentration of oil within the lower layers suggests the presence of aquifers, for example; it might indicate a waterdrive reservoir which, in its turn, leads to a production mechanism type. Additionally, this kind of decision coupled to geological studies may identify probable communicating pathways. Evaluating other properties distribution, such as porosity, permeability and net to gross ratio will support in identifying other possible regions of hydrocarbons’ maturity.
Water-oil-ratio (WOR) forecasting is a method to predict oil production based upon water production trending. A plot of the WOR or Water Cut versus Cumulative Production is the most broadly used technique to evaluate and predict waterflood performance. However, the use of this approach is only possible when a straight line can approximate the functions, allowing us to estimate the recoverable hydrocarbon volumes, commonly expressed as a recovery factor. Kraken can create cross plot graphs, which allows the user to confront parameters and evaluate how they are correlated.
A section plane, commonly known as cutting plane, is the plane that creates a sectional view which shows internal details that cannot be seen from the outside perspective. Regarding reservoir grid visualization, this is very useful to evaluate regions separately. Kraken has a process called “Plane” which enables the user to perform those types of analyses. Kraken is also capable of creating a cut between two wells; this is especially useful to analyze a property over time, so the influence of each well on another can be better understood.
Reservoir simulators use a variety of geometries, ranging from simple structures to complex corner-point systems. In the presence of faults, the physical neighbors no longer coincide with logical neighbors; those physical connections are referred to as non-neighbour connections (NNCs). The fact that horizons cross each other in gridding processes results in the creation of pinch-out layers. Commercial softwares have keywords related to pinched cells that are capable of defining NNCs across cells under the pore volume threshold. That is, direct connections are created across gaps between layers, and the connection is diverted vertically on the local scale. Kraken processes include NNCs visualization throughout the reservoir grid. The NNC tool identifies the regions in the grid where new connections were created due to faulting and presence of other geological features. This kind of analysis is useful to understand reservoir geological modeling as well as comprehend how connectivity/transmissibility parameters driven flow across the reservoir.
3D visualization using Kraken allows the user to investigate a grid block/region through the “Inspection” process. By selecting a cell of desired inspection, it is possible to assess the values of its static and transient properties. An example of a process when “Inspection” can be utilized is the Upscaling, as exemplified in such post. Different inspections are useful to evaluate the values of properties in both original and upscaled grids and then compare how those values change when applying a determined upscaled procedure.
A reservoir grid is a result of reservoir characterization. It enables visualizing and analyzing the distribution of the properties throughout the reservoir cells. Definitions of grid coordinate system vary from one simulator to another and must be clearly defined. Additionally, reservoir grids can be constructed for one-, two- and three dimensions as well as different geometries. Kraken has a capability which enables grid conversion. In the example provided below, the original reservoir grid has been converted to a cell-centered grid type.
A histogram is a graphical method that shows data frequency distribution. It is especially useful when there are a huge number of observations, which means a major quality tool with a vast range of applications. Histograms can be applied to a property distribution in a reservoir grid, mainly because it contains an enormous number of cells. As a manner to construct a histogram, the data must be split into intervals, as illustrated in the graph. In such case, the histogram evinces the oil saturation distribution along the reservoir grid. It is noticeable that most of the cells are in the highest values interval, which suggests a reservoir with good hydrocarbons’ potential.
Kraken 3D visualization capabilities allow Reservoir Engineers to visualize faults, non neighbor connections and pinch-outs, while coloring the cell faces with static or transient properties over time.
Kraken 3D features includes the simultaneous visualization of water, oil and gas saturation, displaying them in a ternary color-scale.
Streamlines are a useful post-processing technique since it allows the engineer to visualize the areas where the oil is being drained and the preferential paths for the water, for example. Kraken is able to recreate the streamlines based on the flow field from Oil, Gas or Water.
Kraken allows the simultaneous visualization of properties at the reservoir grid and the curves in the wells, allowing the engineers to correlate the phenomena happening within the reservoir with the response of the wells.
Frequently two wells in the same reservoir have influence on each other, that can be both an injector helping the producer to keep a good flow rate or two producer wells sharing the available energy in the area. Kraken has a plane cut mode in which two wells are selected and a plane section is created where grid properties like Pressure and Saturation can be visualized over time, so the influence of each well on another can be better understood.
Production trends can be estimated using different equations, such as Exponential and Hyperbolic decline. Kraken has an interface to import historical data and perform such calculations as well as create scripts to perform regression analysis to predict future production.
In this video we show examples of how the user can automate the Workflow by recording macros or implementing more complex routines using Python Scripting and Kraken’s API.
Import a reservoir simulation
Inspect the available data
Visualize grid data in a 3D view
Visualize well data in a 2D plot
Learn about Kraken Panels and Data Organization:
Create a 3D visualization of a reservoir model
Visualize grid properties
Edit window layout
Import a reservoir simulation output file
Drag and Drop a well into the workspace
Select variables to plot
Navigate through multiple wells
Learn how to configure a time plot
Logic behind the property based coloring
Use Windows Editors Panel
Save your preferences
Learn to create a process and how they are organized
Inspect a subsection of a reservoir
Compare properties from a subsection with the entire reservoir
Create a process over an existing process in order to have an smaller part of the reservoir