The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting makes the use of statistical and modern machine-learning techniques a relevant proposition. However, the most current applications of these techniques often ignore the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach, using a modern technique termed functional data analysis (FDA). In FDA, production data are modeled as a time series composed of a sum of weighted smooth analytical basis functions.
Introduction
Many companies have adopted a so-called “data-centric process” for understanding and forecasting in unconventional reservoirs. This data-centric process comes as a consequence of the shortcomings of conventional reservoir-data-analysis and -modeling approaches, which mostly belong to the preshale era.
