Formulating a robust and efficient production optimization and forecasting tool for unconventional oil and gas assets is a challenging task. This is mainly due to the high number of wells involved and the limited predictability of detailed individual well performance until there is sufficient production history.
Current methods to optimize and forecast production rely on type curves and surface network models, neither of which account for changes in the wellbore condition. Generally, these activities are conducted separately and are rarely fully integrated.
A solution that can automatically match real-time performance of an unconventional asset well is presented in this paper. The implemented solution replicates performance of unconventional wells using transient models. Input parameters for all models honour a given range of realistic operational conditions and are automatically regressed to match the historical performance. At a specified interval, historical production is updated in the models and regression is performed automatically to keep the multiple models updated.
This solution can address challenges related to historical well allocation given by lack of remote sensing, by coupling the well model to a network model. The use of automated regression tools to identify outliers reduces the requirement for routine engineering review, thus making forecasting for thousands of wells manageable. Having accurate well models coupled with a network model allows automatic allocation matching at separator levels and hence a calibrated network model leading to production optimization and better field management. Consequently, a robust optimization and forecasting tool for unconventional assets is achieved.