Mathematical Workflow Incorporating PVT/Mass Transfer Rate Models for Subsurface Data Determination - Hon , Vai Yee (2008)
Bottomhole data availability is important for managing reservoir and well deliverability. Adequate knowledge on key well/reservoir parameters assists in both production planning and reserves recovery. However, continuous data acquisition is often where we stumbled, due to a combination of economic, operational/logistical constraints such as economically unjustifiable downhole monitoring devices, risk of fishing/well downtime via well intervention measurement. An alternative to direct bottomhole data acquisition through well intervention is to employ advanced PVT model where surface data and established correlations are used to estimate these downhole data, with reasonable accuracy. To enable an accurate downhole data determination, understanding of wellbore dynamic behavior is crucial in shut-in well performance modeling. However, the major assumption in current PVT equations is instantaneous transformation of one phase (gas) into another phase (oil). During shut-in, this assumption leads to inaccuracy in gas/liquid distribution in the well and hence, an incorrect prediction of interphase level, in-situ density and pressure distribution in the well. This inaccurate PVT characteristic leads to unreliable estimated downhole data, particularly the reservoir pressure. Our research aims to improve the current estimation method by incorporating the time dimension, mass transfer rate, into well performance modeling. The strategy is to develop a novel PVT incorporating mass transfer rate model by employing the basic model of black oil PVT, EoS and mass transfer rate equations. A mathematical correlation which incorporates the time dimension as inherited in the mass transfer theory is devised. Therefore, the model is able to determine an accurate volume of each fluid phase at any node in the wellbore by incorporating the fluid segregation, mass transfer rate and fluid ingress in the reservoir during shut-in. This better description of wellbore dynamic behavior improves the accuracy of well performance modeling ensuring a reliable downhole data determination.