Perpustakaan Digital ITB

Horizontal wells are drilled to enhance reservoir contact and create linear flow for optimal recovery, higher productivity per unit length compared to vertical wells. Horizontal wells improve sweep efficiency and optimizing development and operating costs. However, horizontal wells face challenges like the heel-to-toe effect in homogeneous reservoirs, leading to uneven production and susceptibility to gas/water coning. In heterogeneous reservoirs, permeability variations cause inconsistent fluid flow, which resulted in decreasing oil production. Inflow Control Devices (ICDs) are developed to mitigate these issues by regulating flow through high permeability areas and stimulating tight areas that can be modified based on production and reservoir challenges. This study uses tNavigator 23.4, including the Model Designer, Well Designer, and Assisted History Matching (AHM) modules. It begins with a literature review and data collection, including reservoir models and ICD catalogues. Baseline conditions are set for a conventional horizontal well without ICDs. Initial ICD designs are created and manually optimized. Sensitivity analysis is conducted on 34 constant design cases and 4 manually optimized cases. Advanced ICD design optimization is performed using AHM with Particle Swarm Optimization (PSO) techniques to achieve the best ICD configuration for maximizing oil and minimizing water production. The result shows that the AHM optimization with PSO provides more optimized design compared to manual optimization. The best-case scenario from AHM optimization increases cumulative oil production to approximately 338.08 MSTB and reduces water production to around 2.292 MMSTB. Segment analysis reveals that ICD on segment 5 has the most impact on production, but has a negative correlation to oil production, while segment 3 significantly influences cumulative oil production. In this study, optimization using PSO effectively explores the parameter space, enabling a comprehensive search for the optimal ICD configuration ensuring a more optimized solution. PSO’s iterative optimizations allows for faster approach to the optimal solution compared to traditional methods.