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Characterization of tight-gas sand reservoirs from horizontal-well performance data using an inverse neural network
Gas Science and Engineering Pub Date : 2018-11-01 , DOI: 10.1016/j.jngse.2018.08.017
B. Kulga , E. Artun , T. Ertekin

Abstract Characterization of a tight-gas sand formation using data from horizontal wells at isolated locations is challenging due to the inherent heterogeneity and very low permeability characteristics of this class of resources. Furthermore, characterizing the uncontrollable hydraulic-fracture properties along the horizontal wellbore requires financially demanding and time-consuming operations. In this study, a reservoir characterization model for tight-gas sand reservoirs is developed and tested. The model described is based on artificial neural networks trained with a large number of numerical-simulation scenarios of tight-gas sand reservoirs. The model is designed in an inverse-looking fashion to estimate the reservoir and hydraulic-fracture characteristics, once known initial conditions, controllable operational parameters, and observed horizontal-well performance are input. Validation with blind cases by estimating reservoir and hydraulic-fracture characteristics resulted in an average absolute error of 20%. The model was also tested successfully with published data of an average-performing well in the Granite Wash Reservoir. A graphical-user-interface application that enables using the model in a practical and efficient manner is developed. Practicality of the model is also demonstrated with a case study for the Williams Fork Formation by obtaining probabilistic estimates of reservoir/hydraulic-fracture characteristics through Monte Carlo simulation that incorporates the ranges of observed production performance.

中文翻译:

使用逆神经网络从水平井动态数据表征致密气砂岩储层

摘要 由于此类资源固有的非均质性和极低的渗透率特征,使用来自孤立位置的水平井的数据表征致密气砂岩地层具有挑战性。此外,表征沿水平井眼的不可控制的水力压裂特性需要财务要求高且耗时的操作。在这项研究中,开发并测试了致密气砂岩储层的储层表征模型。所描述的模型基于人工神经网络,该网络经过大量致密气砂岩储层数值模拟场景的训练。该模型以逆向方式设计,以估计储层和水力裂缝特征,一旦已知初始条件,可控操作参数,和观察到的水平井性能是输入。通过估计储层和水力裂缝特征的盲案例验证导致平均绝对误差为 20%。该模型还成功地通过了花岗岩冲洗水库中一口平均性能井的已发布数据进行了测试。开发了一个图形用户界面应用程序,可以以实用和有效的方式使用该模型。通过结合观察到的生产性能范围的蒙特卡罗模拟获得储层/水力裂缝特征的概率估计,还通过对 Williams Fork 地层的案例研究证明了该模型的实用性。通过估计储层和水力裂缝特征的盲案例验证导致平均绝对误差为 20%。该模型还成功地通过了花岗岩冲洗水库中一口平均性能井的已发布数据进行了测试。开发了一个图形用户界面应用程序,可以以实用和有效的方式使用该模型。通过结合观察到的生产性能范围的蒙特卡罗模拟获得储层/水力裂缝特征的概率估计,还通过对 Williams Fork 地层的案例研究证明了该模型的实用性。通过估计储层和水力裂缝特征的盲案例验证导致平均绝对误差为 20%。该模型还成功地通过了花岗岩冲洗水库中一口平均性能井的已发布数据进行了测试。开发了一个图形用户界面应用程序,可以以实用和有效的方式使用该模型。通过结合观察到的生产性能范围的蒙特卡罗模拟获得储层/水力裂缝特征的概率估计,还通过对 Williams Fork 地层的案例研究证明了该模型的实用性。开发了一个图形用户界面应用程序,可以以实用和有效的方式使用该模型。通过结合观察到的生产性能范围的蒙特卡罗模拟获得储层/水力裂缝特征的概率估计,还通过对 Williams Fork 地层的案例研究证明了该模型的实用性。开发了一个图形用户界面应用程序,可以以实用和有效的方式使用该模型。通过结合观察到的生产性能范围的蒙特卡罗模拟获得储层/水力裂缝特征的概率估计,还通过对 Williams Fork 地层的案例研究证明了该模型的实用性。
更新日期:2018-11-01
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