Interact with the results of the Evaluation Matrix via the Completion Optimization dashboard to determine next-generation completion designs and long-term completion design strategy.
Overview
This article describes how to use the Completion Optimization dashboard to identify completion design parameters to maximize economic and/or reserves measures. The completion optimization process uses an Evaluation Matrix to conduct an economic assessment of a large number of potential completion designs and provide the user with an interactive dashboard to illustrate the common design parameters that maximize certain economic outcomes such as Rate of Return (ROR) or Net Present Value discounted at 10% annually (NPV10).
The dashboard automatically updates when the Evaluation Matrix workflows are run.
With outputs such as the next two pictures below, the user can understand the value sensitivity to individual completion paramters and combinations of parameters. Optimal designs are usually chosen to maximize economic indicators such as NPV10 or ROR. In the examples below, the largest (optimal) values are dark red. Optimizing ROR or NPV10 leads to materially different optimal completions. The example ROR graphic (1st picture) has bifurcated optimal designs with condiserably different design charactiertistics (short lateral, high fluid intensity vs. long lateral, lower fluid intensity).
Sometimes the optimal completion designs are a large step-change to existing designs. In these cases, the user may choose a next-generation completion design in the direction of the optimal design (but with a material value increase in a single generation), leaving the optimal design as a multi-generational target. This is done to mitigate operational and/or cost risk with each new generation. Thus, this analysis can be used to guide long-term design strategies and opertional needs.
Pro Tip: The journey to the optimal completion design may be a multi-generational project if there is unacceptable opertional or cost risk associated with the optimal design compared to current practices.
For example, in the first picture below:
- Assume the current design practice (Generation A) is an 8,000' lateral with 10 bbl/ft of fracture fluid
- The optimal design is considered to be a 14,500' lateral with 10 bbl/ft of fracture fluid
- The next generation design (Generation B) may target an 11,000' lateral rather than attempting a 14,500' lateral in a single step-change to the design. If the the 11,000' lateral wells meet expectations (costs and prodcution), then the Generation C designs will move to the 14,500' lateral length.
Completion Optimization Dashboard Details
Accessing the Dashboard:
- 1. Select the 'Dashboards' menu item.
- 2. Select the 'Completion Optimization' dashboard.
There are three tabs in the dashboard:
- Summary
- Evaluation Matrix
- Training Data Set
The Summary tab:
The Summary tab presents the top completion designs. These are not actual wells, but rather the top ranked completion designs from the Evaluation Matrix.
- 1. Select the 'Summary' tab
- 2. Choose the ranking criteria by selecting one of the ranking buttons.
- 3. Review individual completion design details. Note the scroll bars on the bottom and right edge to access additional columns to the right and additional completion desgins on the bottom. The table is sorted by the ranking criteria.
- 4. Review the bar chart which identifies the top 10 completion designs as ranked by the ranking criteris buttons.
The Evaluation Matrix tab:
The Evaluation Matrix tab allows the user to visualize how various completion and geologic parameters influence economic KPI’s.
- 1. Select the 'Evaluation Matrix' tab.
- 2. Review the colorful graphic to understand optimal and near-optimal completion designs.
- A. Choose an x-axis parameter from a drop-down list.
- B Choose a y-axis parameter from a drop-down list.
- C. Choose a color-fill parameter from a drop-down list (the 'Legend' parameter). The legend parameters are limited to results of the Evaluation Matrix process (B90, Capital Cost, ROR, PV10, etc.).
Pro Tip: Note in the example below, with ROR as the color fill, the maximum ROR values are on the edge of the Evaluation Matrix data range. It is possible that a true maximum may occur outside the Evaluation Matrix data range (beyond the x/y axes limits). Be very cautious about expanding the Evaluation Matrix data range beyond the training dataset data range using non-physics-based models (we recommend against this practice). A preferred practice for evaluation of extrapolated designs (designs outside the data range of the training dataset) is to use physics-based models as the trained predictive model to predict production forecasts.
- 3. Review the histogram to understand the distribution of values for a given parameter. The parameter is chosen by selecting from the parameter list immediately to the left of the histogram.
- 4. The slider bars allow the Evaluation Matrix cases to be reduced by limiting the minimum and/or maximum of individual parameters. Click and drag the small vertical line at the end of each dliser bar to change the parameter min/max.
- D. View the resulting number of cases (completion designs) corresponding to the slider bar settings.
- 5. View individual completion design details in a table format. Note the scroll bars at the bottom and right edges of the table to view more columns and rows.
The Training Data Set tab:
The Trailing Data Set tab provides the user with a summary-level understanding of the Training Dataset data ranges, production type curve, geologic extent, and basic completion parameters vs Date of First Production (DOFP). This information is helpful to confirm the Training Dataset has been QC’d and to confirm the data ranges for the Evaluation Matrix are within the Training Dataset data ranges.
- 1. Select the 'Training Data Set' tab.
- 2. Note the map of the training dataset wells.
- 3. Note the average time-normalized production type curve. This curve is generated from the list of training wells.
- 4. Note severals KPI's from the training dataset including minimums, averages, and maximums for each training and predicted parameter.
- A. Note the Well Count.
- 5. Note several graphs of completion and production KPI's vs Date of First Production (DOFP).
- 6. Note the pie charts of primary production phase and fracture fluid type.
Pro Tip: Use the 'Training Data Set' tab to QC and confirm the training data that the training data set is as expected for the completion optimization workflows and evaluation matrix.
