Experience RS

Montney MVA Applications - Raw Production Data Doesn't Make the Cut

Preparing a production data set for multivariable analysis in the Montney poses several challenges. Gas and liquids volumes are seldom metered separately at the wellhead and liquids cut can vary widely, even on a lease level. Training a model based on disclosed production data often underestimates the importance of completions variables on liquids recoveries as targeting on an oil-equivalent basis tends to favor deep, dry gas regions where well performance is largely driven by the over-pressured nature of the reservoir.  

Low-quality production data is converted to two-stream, analytics-ready data after applying RSEG's liquids correction algorithm. This proprietary model incorporates production tests, gas analysis and operator disclosures to better predict liquid production volumes.

The figure below shows a random forest feature importance before and after liquids correction. A large "slice" corresponds to a higher feature importance of the corresponding variable. Using cleaned and corrected data, multivariable models can be trained separately and normalized based on wellhead liquids cut to determine key drivers of performance in each hydrocarbon window. This process is critical in tying together well design, spacing and geological variables and understanding their relationship to well economics and performance within the Montney.

FIGURE 1 | Random Forest Feature Importance in the Montney

Figure 1 | Random Forest Feature Importance in the Montney


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