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基于径向基神经网络的油藏反演方法
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作者 周子琪 查文舒 +1 位作者 李道伦 刘旭亮 《合肥工业大学学报(自然科学版)》 CAS 北大核心 2023年第5期713-720,共8页
文章提出一种基于径向基(radial basis function,RBF)神经网络的油藏反演方法。该方法利用抽样生成的井底压力数据构造RBF神经网络模型,由RBF神经网络预测值与实际观测值的偏差定义目标函数,再利用粒子群算法(particle swarm optimizati... 文章提出一种基于径向基(radial basis function,RBF)神经网络的油藏反演方法。该方法利用抽样生成的井底压力数据构造RBF神经网络模型,由RBF神经网络预测值与实际观测值的偏差定义目标函数,再利用粒子群算法(particle swarm optimization,PSO)对其进行优化,最终得到不确定参数的最优解和反演参数。与多项式拟合方法相比,RBF神经网络方法具有更好的拟合结果和更高的精度,甚至在多项式拟合方法失效时,该方法也能得到很好的模拟结果。油田实际算例表明,该方法具有良好的拟合效果,能大幅提高反演效率,具有很好的应用前景。 展开更多
关键词 油藏反演 径向基(RBF)神经网络 目标函数 优化算法 历史拟合
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Detailed reservoir inversion addressing geological problems in reservoir development 被引量:1
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作者 Shen Guoqiang Meng Xianjun Xia Jizhuang Zhang Xuefang Li Xia 《Applied Geophysics》 SCIE CSCD 2007年第1期58-65,共8页
In the Ken 71 development block, fluvial facies of the Neogene Guantao Formation and delta facies of the Paleogene Dongying Formation are the main pay beds. It is a multiple oil and water system which is complicated b... In the Ken 71 development block, fluvial facies of the Neogene Guantao Formation and delta facies of the Paleogene Dongying Formation are the main pay beds. It is a multiple oil and water system which is complicated by faults. Characteristics of the block include a dense well network, thin reservoirs, complicated horizontal relationships, and small velocity difference between reservoir and non-reservoir. Therefore, it is difficult to conduct detailed reservoir description for subsequent development project adjustment. We demonstrate a stochastic seismic inversion which aims at detailed reservoir description. It is a technology which utilizes multiple wells, seismic data, and geological calibration and integrates with 3D structural interpretation results to build a 3D multi-fault detailed and constrained geological model. On this basis, we adopted stochastic seismic inversion to conduct a multi-stratum parameters inversion such as impedance and lithology. As a result, thin interbedded strata in the block were well resolved and the results demonstrated the importance of detailed reservoir inversion for oilfield development. 展开更多
关键词 Ken 71 block multi-well calibration detailed geological model IMPEDANCE stochastic seismic inversion
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Subtle trap recognition based on seismic sedimentology- A case study from Shengli Oilfield 被引量:10
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作者 Huang Handong Zhang Ruwei +2 位作者 Luo Qun Zhao Di Peng Yongmin 《Applied Geophysics》 SCIE CSCD 2009年第2期175-183,共9页
Seismic sedimentology is the study of sedimentary rocks and facies using seismic data. However, often the sedimentary body features can't be described quantitatively due to the limit of seismic resolution. High resol... Seismic sedimentology is the study of sedimentary rocks and facies using seismic data. However, often the sedimentary body features can't be described quantitatively due to the limit of seismic resolution. High resolution inversion offsets this limitation and is applied to seismic sedimentology to identify subtle traps under complex geologic conditions, thereby widening the applicable range of seismic sedimentology. In this paper, based on seismic sedimentology, seismic phase-controlled nonlinear random inversion is used to predict the sandy conglomerate reservoir of Es3 in the Chezhen depression in Shengli Oilfield. Thickness and sedimentary microfacies maps of sandy conglomerate bodies in several stages are presented and several subtle traps were predicted and verified by drilling. 展开更多
关键词 Seismic sedimentology subtle trap phase-controlled inversion seismic data Jiyang depression
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Seismic to Reservoir Simulation by Cooperative Inversion
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作者 Larry Lines Amir Shamsa 《Journal of Earth Science and Engineering》 2014年第7期410-414,共5页
Cooperative inversion for petroleum reservoir characterization produces an Earth model that fits all available geological, geophysical and reservoir production data to within acceptable error criteria. The mathematica... Cooperative inversion for petroleum reservoir characterization produces an Earth model that fits all available geological, geophysical and reservoir production data to within acceptable error criteria. The mathematical formulation for the inversion requires an appropriate modeling description of both seismic wave propagation and reservoir fluid flow. The inversion requires the minimization of an objective function which is the weighted sum of model misfits for both geophysical and production data. While the complete automation of cooperative inversion may be unrealistic or intractable, geophysical data can provide useful information for enhancing heavy oil production. A methodology is given to demonstrate possible cooperative inversion application in heavy oil reservoirs. 展开更多
关键词 Reservoir characterization cooperative inversion heavyoil 4-D seismology.
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