Chapter 6 Insights: Selecting and Conditioning Analogs

In the practice of type well profile (TWP) development, the quality of the selected analog well set is paramount. Chapter 6 of Monograph 5 is one of the most important and provides a structured approach to selecting and conditioning analogs to create a TWP that best represents the subject wells.

Why Analog Selection Matters

A TWP is only as good as the data that feeds into it. Ideally, we’d have an enormous dataset of fully depleted wells, but reality dictates that we often work with incomplete information. The more restrictive we are in choosing analogs, the better they reflect the expected conditions of the subject wells. However, small analog sets generate low-confidence results because they have a larger standard error of the mean. This balancing act is the heart of Chapter 6.

The Art and Science of Analog Selection

Analog wells should be chosen based on key production determinants, including:

• Geologic and reservoir properties – Formation characteristics, fluid type, and pressure.
• Well density and spacing
• Completion factors – Lateral length and proppant intensity matter, for instance.

Once an analog set is chosen, the next step is conditioning. “Normalization”, often referred to in the context of time-shifting data from wells with different production start dates, is more broadly the process of adjusting production data to account for differences in key determinants. This can be done empirically, using observed production relationships, or analytically, by applying theoretical flow models.

A great example is completed lateral length (CLL). If we have data from wells with lateral lengths from 5,000 to 15,000 feet, but need a TWP for 10,000-foot laterals, we have two options:

1. Restrict the analog set to only wells with similar CLL (reducing sample size).
2. Normalize laterals to reflect expected performance at 10,000 feet.

The latter increases the analog pool but introduces assumptions (in this case, that recovery per foot is independent of lateral length), so careful recognition and validation of implicit assumptions is essential.

Avoiding Pitfalls: Selection Bias and Data Limitations

A common mistake evaluators make is selecting analogs based on performance rather than characteristics. Rationalizing away the wells with lower-than-average EURs can skew results. Instead, it’s crucial to understand why some wells perform worse. If underperformance is due to systematic or random factors that future wells might also experience, those wells should remain in the analog set.

Similarly, data limitations—such as reliance on public datasets with incomplete drilling and completion details—can lead to flawed conclusions. When full well histories aren’t available, indirect differentiators like vintage (date of first production) and location can become important.

Final Takeaways

Selecting and conditioning analogs is as much an art as it is a science. The best evaluators blend statistical rigor with industry experience to create TWPs that are robust, reliable, and—most importantly—fit for purpose. Get this step right, and the rest of the TWP process falls into place. Get it wrong, and no amount of downstream correction will save your forecasts.

Next week’s topic – Analytical Methods for Normalization