Qihong Feng
China University of Petroleum
102 Papers
270 Citations
Qihong Feng is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Oil shale & Adsorption. The author has an hindex of 22, co-authored 102 publications. Previous affiliations of Qihong Feng include Chinese Ministry of Education.
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Papers
Well Control Optimization using Derivative-Free Algorithms and a Multiscale Approach
TL;DR: Three derivative-free optimization algorithms are investigated, chosen for their robust and parallel nature, to determine optimal well control strategies, which encompass the breadth of available black-box optimization strategies: deterministic local search, stochastic global search and Stochastic local search.
23
An integrated workflow for fracture propagation and reservoir simulation in tight oil
TL;DR: Wang et al. as discussed by the authors presented an integrated workflow for fracture propagation and reservoir simulation for tight oil, which can be used to optimize both fracturing and production schedule to maximize production performance and/or economic benefit.
22
Well control optimization considering formation damage caused by suspended particles in injected water
TL;DR: Wang et al. as discussed by the authors developed a method to optimize well control considering formation damage caused by suspended particles in injected water, and predicted the effect of formation damage on the well production performance by coupling an analytical model with a reservoir numerical simulator.
22
Well placement optimization with cat swarm optimization algorithm under oilfield development constraints
Abstract: Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.
22
Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm
TL;DR: In this paper, a displacement balanced degree evaluation method is proposed based on Theil index, which can find the optimal well placement for offshore oilfield to achieve a more balanced displacement and a higher oil recovery.