Collaborative Optimization of Gas Injection Huff-n-Puff Parameters
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Solution Overview
Problem
Current methods for optimizing gas injection huff-n-puff parameters in tight oil reservoirs are inefficient due to single-factor analysis, lack of comprehensive interaction consideration, and difficulty in finding global optimal solutions, often relying on conceptual models and increasing labor and time costs with orthogonal design methods.
Innovation Solution
A collaborative optimization method using a particle swarm optimization algorithm based on a numerical simulation model that accurately characterizes actual reservoirs, considering interactions between parameters to determine optimal gas injection rates, times, and production strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single factor analysis is used to optimize each gas injection parameter in turn, then the optimization process is simple, but the optimization results are not comprehensive due to mutual influence and restriction between different parameters
Solution Approach 1:
The patent segments the optimization problem into two distinct phases: first using single-factor analysis to identify promising parameter ranges, then applying response surface methodology to optimize the interaction effects. This segmentation allows the complex multi-parameter optimization to be broken down into manageable steps that address both simplicity and accuracy requirements.
Solution Approach 2:
The patent merges single-factor analysis with response surface methodology in a sequential two-stage optimization framework. The first stage identifies key parameters and their individual effects, while the second stage combines these parameters to optimize their interactions. This merging of methods produces comprehensive optimization results that account for both individual parameter effects and their mutual influences.
2Loss of time
If orthogonal experimental design method is used for collaborative optimization, then the number of experimental schemes is reduced, but it is difficult to find the optimal global solution effectively
Solution Approach 1:
The patent applies preliminary action by first conducting single-factor analysis to identify the optimal range for each parameter before proceeding to the response surface methodology stage. This preliminary identification of key parameters and their individual optimal values guides the subsequent collaborative optimization, reducing the search space and improving efficiency while maintaining accuracy in finding the global optimal solution.
Solution Approach 2:
The patent transitions from the static orthogonal design approach to a dynamic response surface methodology that can adaptively explore the parameter space. The response surface model dynamically adjusts the optimization search based on the identified parameter relationships, allowing the system to efficiently navigate complex interaction effects and converge to the global optimal solution without requiring exhaustive experimentation.
3Manufacturing precision
If the range of parameter optimization is further subdivided to select more levels, then the global optimal solution can be found, but the number of optimization schemes will greatly increase
Solution Approach 1:
The patent extracts and focuses on the most influential parameters identified through single-factor analysis, separating them from less important parameters. By concentrating the response surface methodology optimization on only the key parameters that have significant effects on the response variables, the patent reduces the number of optimization schemes required while maintaining the ability to find the global optimal solution. This extraction of essential parameters avoids the combinatorial explosion that would occur if all parameters were optimized at multiple levels.
Data Source
AI summary
The invention provides a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs. The method relates to the technical field of oilfield development parameter optimization, including: (1) establishing the numerical simulation model that accurately describes the actual oil reservoirs; (2) determining the optimization parameters of gas injection huff-n-puff, giving the optimization range of gas injection huff-n-puff parameters and other variable constraints, and establishing an optimization objective function; and (3) using the particle swarm optimization algorithm to solve the objective function constructed based on a collaborative optimization model for gas injection huff-n-puff parameters. Then the optimal gas injection rate, gas injection time, soaking time, and production time after the collaborative optimization for gas injection huff-n-puff parameters of the reservoir are obtained.