Abstract
Abstract
Production in an unconventional reservoir varies widely depending on reservoir characteristics (e.g., thickness, permeability, brittleness, natural fracturing), and completion design (e.g., well spacing, frac spacing, proppant volume). A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was developed and deployed in the Montney unconventional siltstone gas reservoir, British Columbia, Canada to identify production zone "sweet spots" from reservoir quality data (i.e., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac spacing, fluid volume, and proppant intensity), which were utilized to enhance and optimize production performance of this unconventional reservoir.
Typical data analytics and predictive ML modeling utilizes all the reservoir quality data and completion quality data together. The completion quality data tends to dominate over the reservoir quality data, because of a higher statistical correlation (i.e., weight) of the completion data to observed production. Hence, resulting predictive ML models commonly underestimate the effects of the reservoir quality on production, and exaggerate the influence of the completion quality data. To overcome these shortcomings, the reservoir quality data and the completion quality data are separated and normalized independently. The normalized reservoir and completion quality data are utilized to identify sweet spots and optimize completion design respectively, through predictive ML modelling.
This novel methodology of predictive ML modeling has identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data. The trained predictive ML model was tested by a blind test (R2=79.0%) from 1-year of cumulative production from 6 Montney wells in the Town Pool, which was also validated by recent completions from 6 other Town Montney Pool wells (R2=78.7%).