Well performance analysis is a critical aspect of oil and gas engineering. It assesses the decline in production rates over time and can reveal patterns and anomalies that may require action. In addition to identifying potential issues, decline curve analysis can help engineers optimize production by evaluating alternative production strategies. However, interpreting and forecasting decline curves can be difficult due to factors such as reservoir heterogeneity, changing operating conditions, and unforeseen events. For more information on how frac fluid recovery services can impact well performance and optimization.
Using decline curves as part of the overall well performance analysis framework is crucial for determining optimal production strategies, improving operational efficiency, and maximizing reservoir recovery. However, it is also essential to consider other techniques that can enhance the accuracy of production forecasts. In particular, incorporating information from a variety of sources—including production logs, wellhead pressure measurements, and reservoir simulations—can provide a more holistic view of the system.
Decline curve analysis can reveal many important trends and characterizations of a well, including the onset of water coning, reservoir heterogeneity, and a changing wellhead pressure environment. Additionally, declining production rates can signal the need for a reservoir stimulation or other types of well interventions. It is therefore important to understand the limitations and uncertainties associated with different decline curve models and the sensitivity of key parameters to ensure accurate predictions.
In this article, we propose a set of metrics capable of revealing changes in well (WPI), reservoir (RPI), and well-reservoir connection (CPI) performance from time-lapse interpretation of permanent downhole and surface pressure measurements in combination with rates. The PTA-metrics enable to separate these contributions, and can be used as a tool for monitoring the well performance under different operating conditions and to detect deviations from the expected well behavior. Furthermore, the proposed metrics are model-independent and rely solely on pressure and rate measurements, which makes them suitable for use in automated interpretation workflows as well as on-the-fly interpretations.
We have tested the PTA-metrics on a number of field cases, and they are shown to be highly reliable for wells with stable transient patterns for long enough durations. We have also found that the early-time noise and wellbore effects usually observed in real transient responses do not affect the resulting metrics for long enough durations.
Moreover, we have also demonstrated that the metrics are sensitive to the choice of time-window used for comparisons. The sensitivity of the PTA-metrics results suggests that the best approach for analyzing time-lapse PTA is to recognize periods with stable pressure derivative patterns and to calculate representative performance indicators based on them. This enables to identify problems as they develop and to initiate an appropriate response before significant damage is caused to the well performance.