Deriving Proven Oil Reserves Comments on the Monte Carlo Simulation Procedure

1993 ◽  
Vol 11 (1) ◽  
pp. 62-65
Author(s):  
Mark Wallace

The definition of reserves categories is frequently related directly back to the probabilistic distribution of reserves in the field. Most developments are planned around the P50 or “most likely” expectation for the field a level which incorporates the Proven plus Probable categories. The Proven category is usually backed out from the resulting reserves distribution by assuming an arbitrary P90 or P80 value, similarly upside or the Reserves including the Possible category are allocated a P20 or P10 value. This approach provides an “accepted” range to the reserves but is essentially reliant upon applying a range to a set of deterministric parameters. This approach assumes the basic principles of reservoir description are correct and can be applied at all confidence levels (P90-P10). In complex reservoirs this is less of a valid assumption, and running deterministic cases using pessimistic and optimistic data interpretations is the realistic way to determine the reserves range for the field.

Author(s):  
Guilerme A. C. Caldeira ◽  
JoaquimAP Braga ◽  
António R. Andrade

Abstract The present paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modelled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.


2011 ◽  
Vol 305 ◽  
pp. 154-158 ◽  
Author(s):  
Xing Lei Hu ◽  
Jia Xuan Chen ◽  
Ying Chun Liang

This paper provides a review of Monte Carlo (MC) method and its applications in mechanical engineering. MC simulation is a class of computational algorithms which require repeated random sampling and statistical analysis to calculate the results. The basic principles, formulas and recent development of Monte Carlo method are firstly discussed briefly, and then the applications of MC simulations in the design and manufacturing of nanostructures are reviewed. Finally, we briefly introduce MC simulation of morphology evolution of machined surface, which come from our recent work.


1989 ◽  
Vol 18 (4) ◽  
pp. 944-951 ◽  
Author(s):  
R BAILEY ◽  
C OSMOND ◽  
D C W MABEY ◽  
H C WHITTLE ◽  
M E WARD

Author(s):  
Ma Yupeng ◽  
Zhang Jianguo ◽  
Qiu Jiwei

Vibration reliability analysis of gear sets considering various kinds of nonlinear random factors is essential for the safety of gear driven systems. In this paper, a rational definition of gear sets vibration reliability was presented at first by taking all kinds of vibration responses including displacement, velocity and acceleration into account uniformly by treating them as a series system with statistically independent components. According to the given definition, a systematic analyzing scheme for the vibration reliability of gear sets was proposed. Vibration reliability estimated via the analyzing scheme would make it conservative but more safely in design of gear driven systems. Subsequently, both analytic and numerical methods for gear sets vibration response reliability estimation were carried out based on the proposed analyzing scheme. The analytic method is suitable for the situations that the vibration responses of gears sets under random circumstances are stationary stochastic responses. While, the numerical method named Multi-crossing Monte Carlo Simulation (MULCMCS) can well solve the reliability estimating problems even when the vibration responses of gear sets are nonstationary stochastic processes. Finally, for illustration, a numerical case of analyzing the vibration response reliability of a single degree-of-freedom (DOF) gear set was given to demonstrate the effectiveness of the MULCMCS method.


1997 ◽  
Vol 50 (11S) ◽  
pp. S168-S173 ◽  
Author(s):  
H. J. Pradlwarter ◽  
G. I. Schue¨ller

A numerical procedure of evaluating the exceedance probabilities of MDOF-systems under non-stationary random excitation is presented. The method is based on a newly developed Controlled Monte Carlo simulation procedure applicable to dynamical systems. It uses “Double and Clump” to assess the low probability domain and employs further intermediate thresholds to increase the efficiency of MCS for estimating first passage probabilities. Applied to a hysteretic type of MDOF-system, the method shows good results when compared with direct MCS.


2019 ◽  
Vol 2 ◽  
pp. 100006
Author(s):  
Patrick Y. Yang ◽  
Cerintha J. Hui ◽  
Daniel J. Tien ◽  
Andrew W. Snowden ◽  
Gayle E. Derfus ◽  
...  

2008 ◽  
Vol 575-578 ◽  
pp. 627-632 ◽  
Author(s):  
Shi Xing Zhang ◽  
Shao Kang Guan ◽  
Xin Tian Liu ◽  
Chun Li Mo

A method of Monte Carlo combined with welding experiments was adopted to study the grain size and microstructure in welding heat affected zone of the ferrite stainless steel. Firstly, the kinetic equation of grain growth was established with the experimental data . Then , a simulation procedure based on the kinetic equation was worked out. Agreement between Monte Carlo simulation result and the real experiment results was obtained.


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