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.     

行政行为的概念及构成要素

行政法学的研究应该尽量放宽视角,适应将来法治建设的需要。我们必须把行政行为的概念放到整个法治体系中甚至整个社会的发展中去研究。     

数学归纳法的几个推广

将通常适用于自然数系的数学归纳法推广到了下有界整数集{n|n∈Z,n≥n0}与上有界整数集{n|n∈Z，n≤m0}及全体整数集Z。并证明了关于命题{P(m,n)}m,n∈z的二元数学归纳法。     

新型钻井钢丝绳卷绳器的设计与应用

针对各油田用卷绳入卷筒和液压卷绳器存在劳动强度大、费工费时、效率低下以及液压油路易出现漏油和性能不够稳定等缺点,设计了新型钻井钢丝绳卷绳器。这种卷绳器使用减速电动机作动力,通过联轴器联接驱动大、小两根轴。大小卷筒均为空套卷筒,3个卷筒可以通过各自的离合器、刹车装置分别控制。大卷筒用来缠绕大绳,2个小卷筒用于缠绕穿大绳的引导小绳。20个井队现场使用表明,以前需要十几人6～7 h才能完成的工作,采用新型卷绳器后只需3人1h即可完成,且操作方便,使用灵活,没有污染。     

ERCM与数据库设计

本文介绍了一种实用的数据库设计方法，它以实体分析法为理论基础，通过对我们日常使用的表进行聚集和归类，完成数据库的逻辑设计，所设计的数据库满足ＢＮＯＦ范式。同时该方法可操作性较强，本文还介绍了自行设计的ＥＲＣＭ数据库辅助设计工具。     

知识经济时代市场营销的新观念

知识经济的到来给市场营销带来了巨大变化；同时，也为营销观念的创新提供了基础，网络营销、知识营销、合作营销等新观念应运而生；企业应当跟上时代步伐，不断适应营销发展的新趋势。     

关于我国资源保障能力的若干思考

资源保障能力是一国综合国力判断的重要指标之一。本文首先界定了资源保障能力的涵义，然后分析了五个对资源保障能力比较重要的影响因素，即资源禀赋、经济发展水平、科学技术因素、政治因素和人们的资源观，最后得出几点我国提高资源保障能力的建议以供参考。     

Steps towards a consumer-driven ‘concept innovation machine’ for food and drink

Innovation is often left to insight and serendipity. A lot of what researchers call innovation is actually a process by which one can make the individual consumer or practitioner more ‘creative’. Although it is important to work with the creative individual in hopes of coming up with the better ‘idea’ and new product/service opportunity, an equally valid albeit novel and counterintuitive approach systematizes creativity in a ‘research-driven machine’. This paper presents an approach to the systematization, based upon the point of view that creativity and innovation comprise the recombination of components into new blends. Given this point of view, to then spur innovation requires a systematic database that the user can access, with tools to help manipulate that database. The paper shows how such a database can be constructed and then used to create a novel product. The approach provides a general framework for the sensory professional to become more involved in the early stages of product development, where the focus is on the conceptual aspects of food features rather than on their physical manifestations in actual products.     

Are Samples in Randomized Controlled Trials of Psychotherapy Representative of Community Outpatients? A New Methodology and Initial Findings.

To determine the extent to which published randomized controlled trials (RCTs) of psychotherapy can be generalized to a sample of community outpatients, the authors used a method of matching information obtained from outpatient charts to inclusion and exclusion criteria from published RCT studies. They found that 80% of the patients in their sample who had diagnoses represented in the RCT literature were judged eligible for at least 1 published RCT; however, 58% of the patients had primary diagnoses such as adjustment disorder or dysthymia, which were not represented in the existing psychotherapy outcome literature. The most common reasons that patients in their sample did not match with published RCTs for psychotherapy are listed, and the implications of these findings for research and practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.