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By Shahab D. Mohaghegh
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In the first part of this series we discussed the Intelligent Iterative Integration (i3) analysis, which integrates the three production data analysis techniques — decline curve analysis, type curve matching and reservoir simulation — using an iterative process. Once the i3 analysis for all the wells in the field is completed, we have the following information for all the wells in the field:
Initial Flow Rate, Qi;
Initial Decline Rate, Di;
Hyperbolic exponent, b;
Permeability, k;
Drainage Area, A;
Fracture Half Length, Xf; and
30-Year Estimated Ultimate Recovery, EUR.
The objective of this segment of the analysis is to put all the above information together in the context of the entire field in order to paint a picture of the status of the field now and in the future. Based on the picture that is being painted and the changes that the field (reservoir) will go through as projected into the future, this segment of the analysis allows engineers and managers to make business and engineering decisions that will maximize the return on the investment.
A set of production indicators (PI) are calculated for each well based on the rate versus time data. These production indicators simply provide a measure of each well’s production capability that might be used to compare them with the offset wells. Following is a list of PIs that is automatically calculated for each well at the start of this procedure:
Best 3 months of production;
Best 6 months of production;
Best 9 months of production;
Best 12 months of production;
First 3 months of production;
First 6 months of production;
First 9 months of production;
First year cumulative production;
3-year cumulative production;
5-year cumulative production;
10-year cumulative production; and
Cumulative production.
Furthermore, results of decline curve analysis are used to calculate the remaining reserves for each well. Remaining reserves are calculated based on the 30-year EUR (which of course can be modified by the user) from which the cumulative production has been subtracted. The remaining reserves can be calculated at different dates.
Using fuzzy pattern recognition technology, Intelligent Production Data Analysis (IPDA) generates 2- and 3-D patterns and maps over the entire field from the production indicators, the remaining reserves and the data that was calculated during the i3 process. It can also develop a set of Relative Reservoir Quality Indices (RRQI) based on the production indicators and allow the user to partition the field into different reservoir qualities in order to identify sweet spots. The collection of maps that are generated during this process will guide the engineers and managers in pinpointing the best infill locations in the field and also identifying the under-performing wells that are prime candidates for remedial operations such as restimulation and workovers.
Figures 1 and 2 are 2-D maps of wells in the Golden Trend in Oklahoma. There are 90 wells operated by three different operators that are included in these maps. In Figure 1 the field has been partitioned based on the best 3 months of production, and in Figure 2 the field has been partitioned based on the best 12 months of production. The RRQI has a lower value when the reservoir has a higher quality. For example, in Figure 1 an average well in RRQI=1 produces about 232 MMcfg, while an average well in RRQI=4 produces about 86 MMcfg during its best 3 months of production. The best 3 months of production for an average well in RRQI of 2 and 3 in this field are 186 MMcfg and 142 MMcfg, respectively.
Figure 2 shows that an average well in RRQI=1 produces about 321 MMcfg in its best 12 months, while an average well in RRQI=4 produces about 120 MMcfg in its best 12 months. The best 12 months of production for an average well in RRQI of 2 and 3 are 301 MMcfg and 232 MMcfg, respectively.
Comparing figures 1 and 2, one can see that as time goes on the size of the partitions change. Although all the partitions are relative (as the name suggests), more productive partitions usually get smaller as some wells move from higher productivity partitions to lower productivity partitions. For example, the three wells in the left side of partition 1 during the best 3 months of production (Figure 1) move to a less productive partition (RRQI=2) during the partitioning of best 12 months of production (Figure 2). The same is true for four wells in the lower right corner of partition 1, Figure 1. These wells move to partitions with RRQI of 2 and 3 in Figure 2.
One of the parameters calculated during the i3 process was the drainage area. Figure 3 shows the application of fuzzy pattern recognition to the drainage area. This figure shows that better wells located in
the north-central part of the field drain as much as 36 acres, while the least productive wells, mainly in the southeastern part of the field, have an average drainage area of about
3 acres.
Figure 4 shows the 3-D view of the drainage area, fracture half-length and permeability patterns developed in the Golden Trend due to production from 90 wells in the past several years. The patterns in this figure show the locations in the field that have higher values of permeability, which seem to be along the eastern and western edges of the field reducing from north to south, while the drainage area and fracture half-length behave in a similar manner, showing larger values toward the northern part of the field, especially on the eastern side.
Having such a view of the formation can help managers and engineers to develop strategies in further developing this field. Using the reserves calculation concept described above, the remaining reserves in this field are mapped and shown in Figure 5. In this figure the remaining reserves are plotted as a function of time, assuming no new wells are drilled.
This figure shows the depletion in the reservoir from year 2005 to 2020, identifying portions of the field that would have remaining reserves that can be produced. As part of IPDA, the user can play "what if" scenarios by identifying locations in the field that are proposed as infill locations. Information from the offset wells is used through a neural network modeling technique in order to estimate the decline behavior of the new location, and the remaining reserves through time are recalculated as shown in Figure 5. The goal is to strategically place the infill wells in places where they would contribute to an efficient depletion of the reservoir.
The "Optimum Infill Locator" is another feature that is currently under development. This feature of IPDA will automatically identify the best infill location in the field through an exhaustive intelligent search process and recommend the best locations in the field for infill well placement.
