A critical review on intelligent optimization algorithms and surrogate models for conventional and unconventional reservoir production optimization

/pdf/data-driven-evolutionary-algorithm-for-oil-reservoir-well-2b6l5k0c.pdf

Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Efficient production optimization for naturally fractured reservoir using EDFM

Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.

Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Optimization of design variables and control rules in field development under uncertainty: A case of intelligent wells and CO2 water alternating gas injection

\n The determination of optimal well locations and well controls in the horizontal-well steamflooding of heavy-oil reservoirs is a meaningful but also challenging task for the complex well types and complicated mechanisms. In this paper, a framework that combines optimization algorithms and the reservoir simulator together is proposed to solve this problem. Two typical algorithms, particle-swarm optimization (PSO) and mesh adaptive direct search (MADS), are both used to study their performance on well-placement optimization, well-control optimization, and the joint optimization of these two aspects. For the joint-optimization problem, both the simultaneous approach and sequential approach are considered. A net-present-value (NPV) formula for evaluation of the steamflooding project is proposed, and optimization runs are conducted for an offshore heavy-oil reservoir by maximizing the NPV of the horizontal steamflooding pilot. The results show that both the PSO and MADS were effective for well-placement/control optimization of the horizontal steamflooding wells. The NPVs were greatly improved throughout the optimization process. The control frequency has great influence on the optimal NPV. Intermittent steamflooding might be a better choice than continuous steamflooding. The steam-injection rate and oil-production rates need to be controlled and decreased at the latter stage for mitigating steam channeling and an ineffective steam cycle between injection and production wells. For the joint-optimization problem, the simultaneous procedure finds the best solution for a case with smaller variable numbers and the sequential procedure performs better for a case with larger variable numbers. The PSO algorithm performs better than the MADS algorithm for more complex problems with larger variable numbers in both the simultaneous and sequential procedures. The sequential procedure is worth considering in practice for problems with large scale and a limited computational budget.

Well Placement and Control Optimization of Horizontal Steamflooding Wells Using Derivative-Free Algorithms

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Well Placement and Control Optimization of Horizontal Steamflooding Wells Using Derivative-Free Algorithms