Four Classifiers Used in Data Mining and Knowledge Discovery for Petroleum Exploration and Development

The application of data mining and knowledge discovery in databases for petroleum exploration and development (PE&D) is becoming promising, though still at an early stage. Up to now, the data mining tools usually used in PE&D are four classifiers: multiple regression analysis (MRA), Bayesian discrimination (BAYD), back-propagation neural network (BPNN), and support vector machine (SVM). Each of the four classifiers has its advantages and disadvantages. A question, however, has been raised in applications is: which classifier is the most applicable to a specified application? This paper has given an answer to the question through two case studies: 1) trap quality evaluation of the Northern Kuqa Depression of the Tarim Basin in western China, and 2) oil identification of the Xiefengqiao anticlinal structure of the Jianghan Basin in central China. Case 1 shows that the results of BAYD, BPNN and SVM are same and can have zero residuals, while MRA has unallowable residuals; but Case 2 shows that the results of only SVM have zero residuals, while BAYD, BPNN and MRA have unallowable residuals. The reasons are: a) since the two cases are nonlinear problems, the linear MRA is not applicable; b) since the nonlinearity of Case 1 is weak, the nonlinear BAYD, BPNN and SVM are applicable; and c) since the nonlinearity of Case 2 is strong, only nonlinear SVM is applicable. Therefore, it is proposed that: we can adopt MRA when a problem is linear; adopt BAYD, BPNN, or SVM when a problem is weakly nonlinear; and adopt only SVM when a problem is strongly nonlinear. In addition, the predictions of the applicable classifiers coincide with real exploration results, and a commercial gas trap was discovered after the forecast in Case 1 and SVM can correct some erroneous well-log interpretations in Case 2.Key words: Multiple regression analysis; Bayesian discrimination; Back-propagation neural network; Support vector machine; Trap quality evaluation; Oil identificatio

Phalanx: A Practical Byzantine Ordered Consensus Protocol

Byzantine fault tolerance (BFT) consensus is a fundamental primitive for distributed computation. However, BFT protocols suffer from the ordering manipulation, in which an adversary can make front-running. Several protocols are proposed to resolve the manipulation problem, but there are some limitations for them. The batch-based protocols such as Themis has significant performance loss because of the use of complex algorithms to find strongly connected components (SCCs). The timestamp-based protocols such as Pompe have simplified the ordering phase, but they are limited on fairness that the adversary can manipulate the ordering via timestamps of transactions. In this paper, we propose a Byzantine ordered consensus protocol called Phalanx, in which transactions are committed by anchor-based ordering strategy. The anchor-based strategy makes aggregation of the Lamport logical clock of transactions on each participant and generates the final ordering without complex detection for SCCs. Therefore, Phalanx has achieved satisfying performance and performs better in resisting ordering manipulation than timestamp-based strategy

Load sharing and system factors for light-frame wall systems

A considerable amount of research has focused on load-sharing and system effects in repetitive-member wood floor systems subject to transverse loading. However, relatively few studies have been conducted to investigate load-sharing and system effects in repetitive-member wall systems which may be subject to combined transverse and gravity (vertical) loading, and which may have different boundary conditions from floors. This research investigates load-sharing and system effects in light-frame wood wall systems and seeks to develop repetitive-member system factors for codified design that rationally account for load sharing and other system effects. These factors are intended for use in the design of individual wall members, much as repetitive-member factors are used in the design of parallel-member floor and roof systems. As part of this research, an analytical model was developed to account for partial composite action, two-way action, and openings in the wall system. The model was validated using experimental test results and was shown to be able to predict reasonably well the response of light-frame wall systems. The model was then incorporated into a Monte Carlo simulation to perform reliability analyses of light-frame wall systems. Since the structural model is complex, and including a time-history analysis within the time-dependent simulation was not computationally practical, the load combination issue was considered separately from the reliability analysis. Sensitivity studies were conducted to investigate how different system parameters affect strength and reliability of light-frame wall systems. The reliability of light-frame wall systems was next evaluated using a portfolio of representative light-frame wall systems designed according to current code provisions. This portfolio approach was also used in evaluating system factors for light-frame wall systems. Thus, two different approaches (a reliability-based approach and a strength-ratio approach) were considered for developing system factors for member-design to account for load sharing, partial composite action and other system effects. Using the strength-ratio approach, a new framework for system factors (i.e., partial system factors) is suggested in which the effects of partial composite action, load sharing, load redistribution and system size (number of members) are treated separately

Coupled Oxidation-Extraction Desulfurization : A Novel Evaluation for Diesel Fuel

This work was financially supported by the National Science Foundation of China (21176021, 21276020, 2187081257). We extend our appreciation to the Deanship of Scientific Research at King Saud University for funding the work, through Research Group Project No. RG-1436-026.Peer reviewedPostprin