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An Automatic History-Matching Workflow for Unconventional Reservoirs Coupling MCMC and Non-Intrusive EDFM Methods

机译:自动历史匹配工作流程,用于非传统水库耦合MCMC和非侵入式EDFM方法

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Technological advancements enable natural gas to be economically produced from ultratight shale rocks.However,due to the limited availability of long-term production data as well as the complexity of gridding,for reservoir simulation studies,in dealing with hydraulic fractures,an efficient automatic history-matching workflow in a probabilistic manner for performing history matching,production forecasting,and uncertainty quantification is highly needed.This can provide critical insights for the decision-making processes.In this study,we present an integrated history-matching workflow through coupling an innovative non-intrusive EDFM(Embedded Discrete Fracture Model)method,proxy modeling of KNN(K-Nearest Neighboring),and MCMC(Markov-chain Monte Carlo)sampling.The non-intrusive EDFM method can be applied in conjunction with any third-party reservoir simulators without the need of changing the source codes.Through non-neighboring connections,EDFM can accurately and efficiently handle hydraulic fractures,which does not require local grid refinement nearby fractures.The design of experiment is applied to perform sensitivity analysis with the purpose of identifying significant uncertain parameters.The KNN is utilized to build proxy model and its quality can be improved through multiple iterations of the workflow.The classic Metropolis-Hasting(MH)algorithm of MCMC is employed to perform sampling and predict posterior distribution of uncertain parameters.An application of the workflow to a horizontal shale-gas well from Marcellus shale is demonstrated and discussed in this study.Gas desorption effect is considered in the reservoir model.Six uncertain parameters are considered for this well including matrix porosity and permeability,fracture half-length,fracture conductivity,fracture height,and fracture water saturation.Based on 10 iterations and 250 simulation cases,52 history-matching solutions with reasonable match results against actual gas and water production rates were identified.After history matching,we performed production forecasting for 30 years using all history-matching solutions under the constraint of constant flowing bottomhole pressure of 500 psi.Reliable P10,P50,and P90 of EUR(estimated ultimate recovery)predictions of gas recovery were determined as 11.9,13.1,and 16.4 Bcf(billion cubic feet),respectively.In addition,the narrower posterior distributions of six uncertain parameters were quantified.The values with the highest frequency for each parameter are determined: porosity is 10.4%,permeability is 0.00034md,fracture half-length is 450 ft,fracture conductivity is 2.85 md-ft,fracture height is 87.5 ft,and fracture water saturation is 38.8%.
机译:技术进步使天然气能够从超轻的页岩岩石中经济生产。然而,由于长期生产数据的可用性有限,而且网格的复杂性,用于储层模拟研究,在处理液压骨折时,有效的自动历史 - 以概率的方式为执行历史匹配,生产预测和不确定性量化的概率方法。这可以为决策过程提供关键洞察。在本研究中,我们通过耦合创新来提供综合历史匹配的工作流程非侵入式EDFM(嵌入离散骨折模型)方法,KNN(K-CORMENT相邻)的代理建模和MCMC(Markov-Chain Monte Carlo)采样。非侵入式EDFM方法可以与任何第三方一起使用水库模拟器无需改变源代码。用非相邻的连接,EDFM可以准确且有效地处理Hydra ulic骨折,这不需要附近的本地网格细化。应用实验的设计以识别显着不确定参数的目的进行灵敏度分析。kNN用于构建代理模型,通过多次迭代可以提高其质量。工作流程。采用MCMC的经典大都会 - 加速(MH)算法进行采样和预测不确定参数的后验分布。在本研究中展示并讨论了Marcellus Shale的水平页岩气的工作流程的应用。在储层模型中考虑了气体解吸效果。将该不确定参数考虑在内,包括基质孔隙率和渗透性,裂缝半长,断裂导电性,裂缝高度和裂缝水饱和。在10次迭代和250个模拟案件上,52历史匹配解决方案具有合理匹配的实际天然气和水产量的结果历史匹配后,我们使用所有历史匹配解决方案在500 psi的恒定流量压力的约束下使用所有历史匹配解决方案进行生产预测。欧元(估计终极恢复)的欧元(估计的终极恢复)预测是分别确定为11.9,13.1和16.4个BCF(亿立方英尺)。此外,定量了六个不确定参数的较窄的后分布。确定每个参数的最高频率的值:孔隙度为10.4%,渗透率为10.4% 0.00034MD,裂缝半长度为450英尺,断裂电导率为2.85md-FT,裂缝高度为87.5英尺,裂缝水饱和度为38.8%。

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