Well productivity reduction over time is one of the critical issues for deep-water wells with huge implications on expected recoveries from these wells. It is important to account for this uncertainty accurately in order to generate reliable production forecasts. Typically, reservoir simulation engineers utilize an expression (e.g. linear or exponential) for modeling changes in skin or Productivity Index (PI) over time as a function of either time or cumulative liquid production or pressure depletion. These commonly used single variable-based PI degradation modeling methods are easy to implement with a flow simulator, but they do not address the multi-dimensional nature of PI degradation which is a result of multiple subsurface effects combined with operational conditions. As a result, these single variable-based modeling methods generally do not have good reliability for predicting PI degradation trend.
This article proposes a method for predicting reduction in producing well's PI by integrating a few key well operating variables (drawdown, borehole depletion, and water cut) into a single mathematical formulation. One of the important assumptions of IPDM is that certain critical drawdown pressure exists for each well in the field. When a well is operated below the critical drawdown pressure, negligible to no PI reduction is observed; however, when drawdown exceeds the critical drawdown value, noticeable PI reduction is seen. The reduction level depends on the ratio of current drawdown pressure to critical drawdown pressure, as well as on pressure depletion and water cut levels. By doing so, the global trend and local granularity of PI reduction are well captured. The concept of critical drawdown used in IPDM is aligned well with the awareness of safe drawdown among Reservoir Management practitioners. Critical drawdown ranges derived from IPDM form a useful analog data set while deciding safe drawdown limits or forecasting PI degradation trends for future wells.
The predictability and generality of IPDM were assessed with historical well test (or PTA – Pressure Transient Analysis) data from more than 50 wells across seven deep-water fields. A detailed workflow of implementing IPDM was demonstrated through one of the field applications. From practical implementation point of view, IPDM method can be easily written in Excel Macro or using a PYTHON script or any other programming language. Once the methodology is coded (or implemented), it can then be plugged in a dynamic flow simulator (e.g. INTERSECT™). A similar approach is usually taken while incorporating single variable-based method for forecasting PI degradation into a flow simulator. Compared to the coupled modeling of geo-mechanical simulator - dynamic flow simulator, the IPDM is a much simpler approach to use and also enables engineers to evaluate the impact of key operating conditions on individual well's PI.
