Injection parameters optimization of crosslinked polymer flooding by genetic algorithm

Lian, Peiqing; Li, Linlin; Duan, Taizhong

doi:10.26804/ager.2018.04.08

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…However, this method has the disadvantages of weak interaction between inert material interfaces and poor matching with fractures. Therefore, inorganic plugging materials, polymer plugging materials and other plugging materials have also extensively been studied (Pandya and Naik, 2016;Lian et al, 2018). Among them, gel material is a typical polymer plugging material, has been developed due to its excellent deformability and compactness, and is widely used in the field of petroleum engineering (Al-Ibadi and Civan, 2013;Ahdaya et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Preparation and investigation of self-healing gel for mitigating circulation loss

Ren

Wang

Long

et al. 2023

Adv. Geo-Energy Res.

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Lost circulation is a common and complex downhole accident in the process of oil and gas drilling. Traditional bridge plugging material has the limitation of poor adaptability to lost formations. Therefore, this study synthesized a new self-healing plugging material to improve the plugging success rate; specifically, the hydrophobic association polymer lauryl methlacrylate-acrylamide-acrylic acid containing Fe 3+ was modified via curdlan to form a composite gel with high strength and self-healing properties. The self-healing time, mechanicalness and rheological properties of the self-healing gel were systematically evaluated. The results showed that the modification of curdlan could significantly improve the mechanical properties and rheological strength of self-healing gel, and the chelating structure formed by Fe 3+ and carboxyl groups could further enhance the mechanical properties of the self-healing gel. Toughness and storage modulus of the LF 0.15 C 2 selfhealing gel with the introduction of curdlan and Fe 3+ could reach 30.2 kJ/m 3 and 3,458 Pa, respectively. Compared with conventional gel materials, composite gels with self-healing properties exhibited better pressure-bearing capacity of 2.5 MPa, and could effectively avoid causing plugging at the entrance of the fractures by high-concentration inert material and improve the pressure-bearing capacity. In addition, the plugging mechanism of the self-healing gel modified via curdlan in formation fractures was analysed in detail. The self-healing gel modified via curdlan prepared in this work has application potential as a lost circulation material in the field of oil and gas drilling.

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Section: Introductionmentioning

confidence: 99%

Preparation and investigation of self-healing gel for mitigating circulation loss

Ren

Wang

Long

et al. 2023

Adv. Geo-Energy Res.

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“…At the same time, the current method is basically static optimization, and the result of dynamic optimization cannot be obtained. (3) Method based on optimization theory: this method applies optimization theory to find the best plan, uses reservoir numerical simulation to predict the production performance of the plan, then calculates the value of the objective function, and finally obtains the best injectionproduction plan [10][11][12][13][14][15][16][17][18]. This method can realize dynamic optimization and can compare more sets of injectionproduction schemes.…”

Section: Introductionmentioning

confidence: 99%

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Tang

et al. 2021

Geofluids

Self Cite

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A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

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“…In the past, due to the limitation of computation capacity, the parallel simulation of large-scale integrated reservoir could not be well applied, and the development of fine numerical simulation technology is inhibited (Jiang et al, 2001;Cao et al, 2002;Pan et al, 2002;Yang et al, 2003). The appearance of high-performance parallel computer brings the reservoir numerical simulation technology into a new era, and makes the research of parallel numerical simulation a hot spot (Dogru et al, 2003;Liu et al, 2011;Tolstolytkin et al, 2014;Lian et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Parallel numerical simulation for a super large-scale compositional reservoir

Lian

Duan

et al. 2019

Adv. Geo-Energ. Res.

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A compositional reservoir simulation model with ten-million grids is successfully computed using parallel processing techniques. The load balance optimization principle for parallel calculation is developed, which improves the calculation speed and accuracy, and provides a reliable basis for the design of reservoir development plan. Taking M reservoir as an example, the parallel numerical simulation study of compositional model with ten million grids is carried out. When the number of computational nodes increases, message passing processes and data exchange take much time, the proportion time of solving equation is reduced. When the CPU number increases, the creation of Jacobian matrix process has the higher acceleration ratio, and the acceleration ratio of I/O process become lower. Therefore, the I/O process is the key to improve the acceleration ratio. Finally, we study the use of GPU and CPU parallel acceleration technology to increase the calculation speed. The results show that the technology is 2.4∼5.4 times faster than CPU parallel technology. The more grids there are, the better GPU acceleration effect it has. The technology of parallel numerical simulation for compositional model with ten-million grids presented in this paper has provided the foundation for fine simulation of complex reservoirs.

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Injection parameters optimization of crosslinked polymer flooding by genetic algorithm

Cited by 5 publications

References 24 publications

Preparation and investigation of self-healing gel for mitigating circulation loss

Preparation and investigation of self-healing gel for mitigating circulation loss

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Parallel numerical simulation for a super large-scale compositional reservoir

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