The variation step adaptive Glowworm swarm optimization algorithm in optimum log interpretation for reservoir with complicated lithology
The variation step adaptive Glowworm swarm optimization algorithm in optimum log interpretation for reservoir with complicated lithology
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DOI：
10.1109/fskd.2016.7603323
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发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Xiuwen Mo;Xiao Li;Qiang Zhang
中科院分区：
文献类型：
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作者：
Xiuwen Mo;Xiao Li;Qiang Zhang
地球物理测井资料反演是油气勘探的重要任务之一。解的模糊性通常是固有的，特别是对于具有复杂岩性的地层。最佳测井解释技术可以有效地减少解释结果的模糊性。为此，将群体智能优化算法之一的萤火虫群体优化算法（GlowwormSwarmOptimization，GSO）引入到测井解释中，利用其较强的局部和全局寻优能力，获得最优解。此外，为了解决后期迭代过程中收敛速度慢的问题，将自适应步长引入萤火虫群优化算法，形成变步长自适应萤火虫群优化算法（VSAGSO），提高了寻优精度和效率。将VSAGSO算法应用于某油田凝灰质砂岩储层进行了试验。该方法综合考虑各种误差和约束条件，可直接计算出凝灰岩含量、泥质含量、骨架矿物含量、孔隙度等储层参数的优化结果，与岩心资料吻合较好。
Inversion of geophysical logging data is one of the most important tasks in oil and gas exploration. Ambiguity is usually inherent for the solutions, especially for formation with complex lithology. The optimum log interpretation technique can effectively reduce the ambiguity of the interpretation results. Therefore, the Glowworm Swarm Optimization (GSO), one of the swarm intelligence optimization algorithms, is introduced into the log interpretation to obtain the optimal solution by virtue of its strong ability both in local and global optimization. Moreover, in order to solve the problem of slow convergence speed in the later iteration process, adaptive step is integrated into glowworm swarm optimization to form the Variation Step Adaptive Glowworm Swarm Optimization (VSAGSO) algorithm, which improves the accuracy and efficiency of optimizing. VSAGSO algorithm is applied for test in the tuffaceous sandstone reservoir in a certain oilfield. Comprehensively considering all kinds of errors and constraints, it could directly working-out the optimized results of reservoir parameters such as tuff content, shale content, skeleton mineral content and porosity in well accordance with the core data.