An effective integration optimization algorithm for regional fracturing design and drilling placement

Wang, Huijun; Qiao, Li; Zhang, Jun; Lu, Shuangfang; Chen, Fangwen; Liu, Yang; Fu, Zheng

doi:10.1016/j.jngse.2022.104505

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Studies of well placement optimization using genetic algorithms (GAs) [10][11][12] as optimization algorithms have been carried out by several researchers using a variety of methods. A methodology is developed to deploy high-deviation wells and horizontal wells [13].…”

Section: Introductionmentioning

confidence: 99%

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Irawan,

Wayo,

Satyanaga

et al. 2024

Energies

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Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG.

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Section: Introductionmentioning

confidence: 99%

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Irawan,

Wayo,

Satyanaga

et al. 2024

Energies

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“…The first horizontal well was constructed in Daqing Oilfield in 1991, and its output was 4-8 times that of the surrounding vertical wells. With the continuous improvement in logging technology and instruments, rapid progress has been made in recent years in horizontal-well-related technologies and construction [1][2][3], for instance, in horizontal well logging interpretation [4][5][6][7], drilling technology [8][9][10], completion technology [11][12][13], fracturing technology [14,15], development program optimization [16,17], and geologic steering [18,19]. Although many scholars have studied sedimentary microfacies, most have focused on vertical wells, and relatively few have studied sedimentary microfacies by combining horizontal and vertical wells to establish the pattern for identifying sedimentary microfacies of single horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, China

Fan,

Dong,

Zhao

et al. 2023

Energies

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The study of sedimentary microfacies of horizontal wells is important for improving oil recovery using horizontal well technology. Vertical well data alone do not provide accurate enough information to determine the sedimentary microfacies of horizontal wells. Therefore, a comprehensive method combining the data of both horizontal and vertical wells was established to identify sedimentary microfacies of horizontal wells and applied to a single horizontal well in the Daqing oilfield in China’s Songliao Basin. The results identified the study area as a delta sedimentary environment, mainly subdivided into four microfacies types: a distributary channel, the main overbank sand, the overbank sand, and an interdistributary bay. The criteria for identifying each sedimentary microfacies were established. Among them, the criteria for identifying distributary channels include a natural gamma value continuously less than 90 API; a resistivity value continuously greater than 11 Ω·m; a logging curve, which is typically bell-shaped or box-shaped with very high amplitude and amplitude difference; a mainly siltstone lithology; and a total hydrocarbon content (Tg) continuously greater than 3%. The variations in the two types of channel boundaries (narrowing of the channel boundary and reverse extension of the bifurcated channel boundary) were corrected. The research results can provide guidance for the efficient development of favorable reservoirs in oilfields using horizontal well technology.

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Practice and Understanding of Horizontal Well Drilling and Production Integration in Overseas Petroleum Projects

Hou,

Yue,

Zhang

et al. 2024

Springer Series in Geomechanics and Geoengineering

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An effective integration optimization algorithm for regional fracturing design and drilling placement

Cited by 6 publications

References 49 publications

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, China

Practice and Understanding of Horizontal Well Drilling and Production Integration in Overseas Petroleum Projects

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