Productivity index (PI) reduction is a recognized phenomenon in oil and gas production to the extent that production becomes uneconomic. Productivity index (PI) decrease may be caused by several factors arising from reservoir, completion, and operational issues. The reservoir-related factors include compaction, fines migration, pressure support and multi-phase fluid flow. The completion-related issues arise from frac-pack geometry and stress-sensitivity of proppant conductivity. Finally, operational issues such as pressure drawdown at the sandface and induced flux (the movement of fluids across the completion) may also play a large role in PI degradation. Understanding the interactions of different parameters controlling the PI behavior and the resultant well performance is paramount for maximizing the ultimate recovery and net-present value or NPV. PI modeling may offer several challenges because it involves non-unique solutions for reservoirs and near-wellbore and well-completion status, in addition to manual data entry subject to human errors, uncertainties in reservoir parameters, and poor quality field data.
This study presents a workflow for modeling well PI degradation for an over-pressured gas/condensate reservoir in deep offshore Gulf of Mexico. An automated procedure was tailored and applied to match production history by adjusting both the reservoir and well completion parameters. Among the reservoir parameters considered were horizontal stresses, rock compressibility, absolute permeability, relative-permeability endpoints and curvature, porosity, stress-sensitive permeability and porosity. The well completion parameters included fracture length, height, width, conductivity, and stress. A "Tabu Scatter Search" optimizer engine (April et al, 2003) selected the optimal values of a set of input parameters to minimize the objective function, i.e. the error between the measured and calculated PI values.
Both reservoir and well completion parameters contributing to PI degradation were identified, and compared to those obtained by alternate methods. The relative and combined contributions of stress-sensitive reservoir compressibility, permeability, porosity, and fracture conductivity all helped understand well performance and guide decisions towards optimum well operating envelope.
