6 February 2011
In this my second in a sequential blog on creating Quality Static Conceptual Fracture Models, I focus on continuation of the first 4 elements that are the more standard ones included in most models, that being fracture fill and fracture morphology, Table 1.
Table 1. The list of key elements included in a quantified complete Static Conceptual Fracture Model.
There are a couple approaches to defining fracture filling and morphology, both allow for the predictability of flow potential of the fracture system vertically and laterally in the reseroir. The first is difined by direct rock observation in outcrop or core, Figure 5. The classification is based on degree of both mineralization in the fractures or deformation along the fractures, Figure 5. Examples of various of these fractrure morphologies in core and outcrop photos is shown in Figure 6.
Figure 5. Fracture Morphology Types derived from direct observation of fractures in outcrop and core as defined in Nelson (1985).
Figure 6. Core and outcrop examples of the fracture morphologies defined in Figure 5. These various morphologies are shown to be very important in defining the flow capabilities of the fractures in the subsurface in Nelson (1985 & 2001).
However, much of our subsurface fracture data today comes from Borehole Image Logs. The second approach defines how filled the fractures are. In either the ultrasonic or resistivity image logs the output of fracture type is primarily in the form of fracture filling (closed, open, or partially open). It is often difficult to tell what type of filling actually exists ; deformation or mineralization. As a result, we open map the fracture filling as the percentage of open + partially open fractures interpreted within the well as interpreted in the image log. Figure 7 shows an example of a map of open fractures in a field area.
Figure 7. An example of a map of the % of Open + Partially Open Fractures within a field area as interpreted in borehole image logs.
As with the fracture intensity displays in blog installment 1 last month, fracture fill data can be displayed in histogram format for a better visual feeling on relative distribution from one well to another, Figure 8. These data can also be displayed for each mechanical and diagenetic unit within the reservoir in map or histogram form as displayed here, or displayed along wellbores as companion fracture intensity curves for both all interpreted fractures and just open and partially open fractures.
Figure 8. An example of histogram display of the % cemented fractures (no flow) which is the inverse of open and partially open fractures. This display shows the difference in fracture flow capability from well to well. This display is from the same general area shown in Figure 7.
However, this data is intended to be used to constrain reservoir simulation models. Therefore, the simulation engineers often require a measure of flow uncertainty associated with the fractures within the reservoir. As a result, the data is often turned into a flow capability uncertainty diagram such as shown in Figure 9.
Figure 9. Shown is an example of a fracture filling display used to suport flow simulation modeling. The filling data is turned into a % of fractures per unit on average that will flow, that will not flow, and that we are uncertain as to whether they will or will not flow in the subsurface. This allows for multiple runs spanning the range of uncertainty of this parameter.
In next months blog, I will address the element of selection of the Static Fracture Modeling Style.
8 January 2011
“Creating a Static Conceptual Fracture Model for Reservoir Simulation, Part 1”
Welcome to my first blog on fractured reservoirs. I intend to blog monthly on current fractured reservoir issues as well as to present installments of longer technical issues and presentations. In this blog entry, I initiate a discussion on my thoughts on creating a quality Static Conceptual Fracture Model. These models are used to further exploration efforts in fractured reservoirs and to provide a quantitative data base from which to create a quality reservoir simulation model.
For this initial month, I wish to lay out the key elements that must be addressed to create a quality Static Fracture Model and point out how this differs from the standard work and presentations that are done. In subsequent months we will detail most of these issues presented in the key elements list in terms of major issues involved and how they can be quantified.
I believe that the key elements involved in a quality Static Conceptual Fracture Model are listed in Figure 1. This list highlights major topic areas each of which contains multiple data sets, analysis techniques and computer applications.
Figure 1. Shown is a list of the key elements needed to create a quality Static Conceptual Fracture Model. Each of these elements needs to be quantified to be used effectively.
Highlighted in green (first 4 parameters) are the standard approaches performed in a typical fractured reservoir study. These standard approaches are generally anecdotal and poorly quantified.
The following is a field example addressing the first typical elements of this type of study from a carbonate field in the Middle East, Figures 2 through 4. Included are fracture orientation & its’ distribution (Figure 2), fracture spacing or intensity & its’ distribution (Figures 3&4).
Figure 2. Shown is an example of a well-constrained map of fracture orientation and its’ distribution in the central portion of a Middle East carbonate reservoir. This map is controlled by data from 32 horizontal wells. The entire field area is controlled by 54 horizontal FMI wells representing 4,500 m of interpreted image logs. Typical studies of fractured fields have much fewer data sets to integrate. These data are compared with seismic data, production rates, water-cuts, and water flood performance.
Figure 3. Shown is the horizontally measured fracture intercept rate (geometrically corrected) for the same field area presented in the central portion of Figure 2. This presentation shows significant variation in fracture intensity and appears to be related to variations in depositional facies.
Figure 4. Shown is the distribution of average fracture intensity by well for the larger data set shown for the field depicted in Figures 2 & 3. This is a typical distribution form for must fractured reservoirs.
While this field example is a well-constrained example it is only the first of the elements needed in a creating a quality fracture model. Future blog issues will address the other elements needed in the upcoming months.