The increasing attention and development of unconventional resources has many in the industry searching for suitable analogs to supplement their evaluation. A common approach is the use of type wells. Type wells are created by averaging the rate of several analogous wells. This type well rate and corresponding volume is used as a benchmark for evaluating and guiding forecasts for similar wells. The concept of type wells is not new but there are aspects that can be refined to improve results.
The current industry practice has a flaw that when combined with development practices will provide inaccurate results. When creating a type well from historical data only, forecasts are implicitly calculated for wells that do not have enough production to reach the end of the type well time interval. Adding to this is the fact that operators will optimize profit by drilling their best wells first. In this instance the type wells will have a greater rate profile and expected ultimate recovery (EUR) than the underlying data will support. This is because the implicit forecasts for the newer, less productive wells are created from the older, better wells. Conversely, type wells will under-predict rate and EUR in technical plays where performance improves with experience. This paper proposes an approach to address the flaw.
When historical production data is merged with reliable production forecasts to build a type well, the resulting type well is the best available representation of the underlying data. Measures to ensure accurate forecasts on individual wells are recommended.
As an extension to predicting a single rate for similar wells, type wells are also employed to predict different percentile outcomes for similar wells. A common method considers all of the data and calculates a percentile at each time step (Time Slice approach). This approach does not produce consistently reliable results. This paper will propose an alternative approach to creating Type Wells at varying percentiles by analyzing actual wells whose outcome is close in value to the desired percentile.
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