Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

宁夏宁东供水工程主机设备形式参数选择与国际招标

宁东供水工程供水水泵共有卧式和立式离心泵2种方案可供选择,也是工程论证时争论的焦点,经综合比较及主机设备的国际招标,最终确定了奥地利安德里兹公司的卧式双吸中开式离心泵为宁东供水工程的供水水泵。     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

DFIT测试在长庆气田开发中的应用

与常规的小型测试压裂不同，流体注入诊断测试（DFIT）是通过关井阶段出现的拟线性流或拟径向流阶段求取储层压力，采用改进的梅耶霍夫方法（M-method）解释储层渗透率。以长庆榆林气田一口开发井为例，依据DFIT的技术原理和测试步骤，对该井测试压降数据进行解释分析，结果可信度较高，证明了该项工艺在长庆低压低渗油气田的可行性。     

横泉水库大坝土料碾压试验

在取得土料场土料物理力学性质参数的情况下，遵循碾压试验基本原理、方法及参数组合，进行了现场碾压试验，通过试验成果的分析，得出了大坝碾压试验结论，为大坝土方填筑提供了技术依据。     

焊膏印刷工艺参数的选择与优化

表面贴装工艺流程的关键工序之一就是焊膏印刷,其工艺控制的好坏直接影响着装配的线路板的质量。通过对印刷工艺参数的分析,利用试验设计的方法来找出关键工艺参数,并加以优化设定,从而为控制工序参数奠定基础。     

电信企业信息化统一模型构建策略

电信企业信息化的实质就是赋予电信企业精细化的企业资源整合能力，本以此认识为出发点，通过定义基本的信息化业务元素，需求参数和基本信息处理机制，构建出带有行业共性的电信企业信息化统一模型，该模型针对目前电信企业信息化中常见的和潜在的问题提供了有效的解决措施。     

川西新场气田JS_2~1气藏测井多参数评价方法

针对川西新场气田JS12气藏开展了多参数的综合评价研究,多参数包括储层参数、裂缝参数、储层分类厚度参数。首先,开展了储层参数及裂缝参数的计算,以岩心分析结果为依据,建立了孔隙度、渗透率、含水饱和度计算模型。利用铸体薄片进行了微裂缝识别,对样品的裂缝参数和测井曲线进行相关分析,建立了测井计算裂缝参数的数学模型;其次,对储层进行聚类分析,通过聚类将储层分为三类;最后,将计算的裂缝参数、储层分类中的Ⅰ类储层厚度、孔渗饱等参数进行正态化处理,拟和测试的无阻流量与这些参数建立多元回归计算模型,即综合含气指标的数学计算模型。以综合含气指标为主要依据,进行储层综合分类和评价,指出了有利的含气区域和可供挖潜井的建议。该方法在指导气藏开发中获得了良好效果。     

热管传热性能对小型热管换热器散热效能影响

在零重力辅助下,小热管热阻是小型热管换热器散热效能的重要影响因素之一。基于集总参数法对于换热器系统数值计算的高效性思想,建立了热管、热管换热器的集总参数模型。对同一热管换热器选用不同传热性能的热管,在不同传热温差和流体质量流速下,就其散热效能进行了数值计算。计算结果表明,在零重力辅助下,热管换热器管内热阻存在一临界值Rcri。当热阻RHP≤Rcri时,管外热阻起主导作用,热管换热器的散热效能εh几乎不随热管管内热阻的值而变化。     

Effect of oxygen deficiency (δ) on transition temperature of yttrium cuprate superconductors

The nature of pairing mechanism as well as transition temperature of yttrium cuprates is discussed using the strong coupling theory. An interaction potential has been developed for the layered structure with two conducting CuO₂(a–b) layers in a unit cell. The interaction potential properly takes care of electron-electron, electron-phonon and electron-plasmon interactions. Furthermore, the electron-phonon coupling parameter (λ), the modified Coulomb repulsive parameter (μ*) and the 2D acoustic phonon (plasmon) energy as a function of oxygen deficiency is worked out. Finally, the superconducting transition temperature (T _c) is then evaluated by using these coupling parameters and obtainedT _c = 95(92)K for Y(Yb)Ba₂Cu₃O_7−δ superconductors withδ = 0·0. The model parameters estimated from the layered structure approach are consistent with the strong coupling theory. The result deduced on the variation ofT _c withδ are in fair agreement with the earlier reported data on yttrium cuprates. The analysis of the above results are discussed.