Well Placement Optimization Using Dynamic Productivity Index
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Solution Overview
Problem
Current well placement optimization methods in oilfields rely heavily on static parameters, which are insufficient for dynamic reservoir conditions, leading to suboptimal well placement and increased costs due to the lack of consideration for changing fluid flow and saturation dynamics.
Innovation Solution
The development of a system and method using total dynamic productivity index maps to determine the optimal placement of wells by calculating a total dynamic productivity index for each coordinate, incorporating dynamic parameters such as productivity index and pressure drawdown, to guide well placement decisions and reduce the need for extensive simulation runs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static parameters are used for well placement optimization, then the placement process is simpler and faster, but the accuracy and adaptability to dynamic reservoir conditions deteriorates
Solution Approach 1:
The patent pre-calculates and stores productivity index values for all grid blocks in the reservoir model before the optimization process. This preliminary computation of dynamic parameters allows the subsequent well placement optimization to use these pre-computed values, avoiding the need for extensive simulation runs during the optimization itself, thus resolving the contradiction between accuracy and computational complexity
Solution Approach 2:
The patent creates a simplified reservoir model that copies essential dynamic characteristics (productivity index, pressure drawdown) from the full dynamic simulation. This copied representation allows optimization algorithms to work with dynamic parameters without requiring the computational overhead of running full dynamic simulations, achieving both accuracy and efficiency
2Measurement precision
If extensive simulation runs are performed to account for dynamic reservoir conditions, then the well placement accuracy improves, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary calculation of productivity index and pressure drawdown for all grid blocks before optimization. These pre-computed dynamic parameters are stored and reused during the optimization process, eliminating the need for repeated simulation runs and significantly reducing computational time while maintaining precision
Solution Approach 2:
The patent introduces dynamic parameters (productivity index, pressure drawdown) into the well placement optimization process. By using dynamically updated parameters that reflect changing reservoir conditions, the system achieves higher placement precision without requiring extensive simulation runs, as the dynamic state is captured through efficient calculations
3Adaptability or versatility
If dynamic parameters are incorporated into well placement optimization, then the adaptability to changing reservoir conditions improves, but the complexity of the optimization system increases
Solution Approach 1:
The patent changes the parameters used in well placement optimization from static properties to dynamic parameters (productivity index, pressure drawdown). This parameter transformation allows the system to adapt to changing reservoir conditions while maintaining a relatively simple optimization framework, as the dynamic information is captured through calculated parameters rather than complex dynamic modeling
4Measurement precision
If the productivity index is calculated for each coordinate using dynamic parameters, then the identification of sweet spots improves, but the computational resources required increase
Solution Approach 1:
The system pre-calculates the productivity index for all grid blocks using dynamic parameters before the optimization process. This preliminary computation, though energy-intensive, is performed once and the results are stored for reuse, avoiding repeated calculations and significantly reducing the overall computational energy required while maintaining high accuracy in sweet spot identification
Data Source
AI summary
Embodiments of the invention include systems, methods, and computer-readable mediums for optimizing the placement of wells in a reservoir. Embodiments include, for example, determining for a reservoir a layer productivity index for coordinates of a reservoir using reservoir metrics and specified well spacings associated with the reservoir, determining a total dynamic productivity index for the coordinates based on the layer productivity index, and determining placement of one or more wells responsive to the total dynamic productivity index and defined spacing between the one or more wells. Embodiments further include, for example, generating a production analysis report for the reservoir that includes an assessment of well placement and generating a wells placement map using one or more total dynamic productivity index indicators.