scholarly journals Analysing the sensitivity of input parameters for oil reserve estimation of DQ oil field in conjunction with Monte Carlo simulations

2015 ◽  
Vol 18 (2) ◽  
pp. 5-23
Author(s):  
Xuan Van Tran ◽  
Ngoc Ba Thai
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Oil And Gas ◽  
Water Saturation ◽  
Input Parameter ◽  
Simulation Software ◽  
Oil Field ◽  
Monte Carlo Algorithm ◽  
Reserve Estimation ◽  
Input Parameters

The Monte Carlo algorithm is used widely in the areas of humanlife, such as currency risk calculations, mathematical probability and statistics, atmospheric research, materials research applications in laser ... In the oil and gas sector, the Monte Carlo algorithm is mostly applied in oil and gas exploration. Worldwide there are many researchs worked on the Monte Carlo algorithm application through oil and gas reserve estimation. In Vietnam, the reserve estimation with the support of simulation software is no wonder, particularly Monte Carlo algorithms have been adopted on the reserve estimation for many years. However, this algorithm is just applied to predict results. The analysis of the influence of each input parameter on the calculation for reserve estimation is quite restricted. Therefore the article refers to the sensitivity analysis of each input parameter for oil reserve estimation of DQ oil field in conjunction with Monte Carlo simulations in the territory of Vietnam in order to improve reliability of the results. Analyzing results the effects of the input parameters to the reserve estimation by volumetric methods in DQ oil field shows there are five effect parameters (Bulk rock volume, initial water saturation, porosity, formation volume factors, the net to gross thickness ratio), porosity which influence range varies from 0.66 ÷ 0.83 is the greatest impact factor to the assessment results.

DEFINITION OF PETROLEUM RESERVES USING PROBABILITY ANALYSIS

1979 ◽  
Vol 19 (1) ◽  
pp. 197
Author(s):  
B.G. McKay ◽  
N.F. Taylor
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Oil And Gas ◽  
Water Saturation ◽  
Cumulative Probability ◽  
Reservoir Properties ◽  
Simple Method ◽  
Volume Porosity ◽  
Cumulative Curve ◽  
Definition Of

The realistic estimation of reserves and resources is important to many diverse groups including explorers, producers, auditors, taxmen, bankers, shareholders and governments. Reserves data are used in different ways for a variety of reasons and often the figures are used without adequate definition and/or recognition of the uncertainties associated with them. Any calculation method which fails to consider the uncertainties involved, cannot portray a realistic assessment of reserves.Esso Australia Ltd. uses a relatively simple method to generate probability distribution curves in order to allow a more perceptive definition of the range of reserves for the offshore oil and gas fields in the Gippsland Basin and Esso is advocating wider petroleum and mineral industry acceptance of this approach.The method involves defining data distributions for each of the reservoir properties (volume, porosity, water saturation, compressibility and recovery factor) which are multiplied using Monte Carlo Simulation to generate the distribution of reserves. Actual input consists of data from:A high confidence area immediately surrounding well control, where the rock volume is relatively closely defined and the distributions of the other parameters, with the exception of recovery factors, reflect the observed variations.Other areas which are only seismically controlled, where the data ranges reflect both observed and interpreted variations in volume (gross and net), porosity, water saturation, compressibility and recovery factor.The curves generated for each area are then added by Monte Carlo Summation to yield the probability distribution of reserves for the whole field. In this method all available data are used and fewer subjective decisions are necessary. The computer generated distribution curves plot cumulative probability on the y-axis versus reserves on the x-axis. The curves allow the evaluation of the entire range of potential reserves, are valuable in economic and risk assessments and allow for more consistency in defining reserves for reporting purposes. The different categories of reserves, viz. "proved", "probable" or "possible", can be specified from the total field curves at defined probabilities. Moreover, the slope of the cumulative curve provides a direct indication of the level of knowledge of the field or parts of it.

scholarly journals Real options analysis in the engineering company practice

2013 ◽  
Vol 61 (7) ◽  
pp. 2303-2309
Author(s):  
Václav Klepáč ◽  
Petr Kříž ◽  
David Hampel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Real Options ◽  
Net Present Value ◽  
Simulation Software ◽  
Real Options Analysis ◽  
The Real ◽  
Investment Projects ◽  
Research Problems ◽  
Path Dependent

In this paper, we deal with the real options analysis of selected investment projects. This approach is supplemented and compared to calculations of the net present value (NPV). Two research problems are analyzed: acquisition of the simulation software for the foundry industry in the sense of the expansive options and options on leaving the project in the case of acquisition of the spectrometer. For the option valuation, there were used analytical and numerical methods like the Black-Scholes model, binomial model and Monte Carlo simulations. In the case of binomial pricing model we used modification describing the behavior of the project’s cash-flow (CF) due to capacity of the company, path-dependent addiction and embedded option barrier. To extend the application of the real options analysis, we propose procedures for sensitivity analysis and option pricing based on Monte Carlo simulations for particular case of stochastic volatility.

Phase Behavior of Pure Components - Carbon Dioxide, n-Decane and Argon: Test of Simulation Software-Molecular Simulation Techniques (Gibbs Ensemble Monte Carlo Simulation)

2020 ◽  
Author(s):  
Raphael Nnam ◽  
Victor Ejeke ◽  
Umunnakwe Egele
Keyword(s):  
Carbon Dioxide ◽  
Monte Carlo ◽  
Phase Behavior ◽  
Oil And Gas ◽  
Simulation Software ◽  
Constant Volume ◽  
Gibbs Ensemble ◽  
Ensemble Monte Carlo ◽  
Simulation Techniques ◽  
Gibbs Ensemble Monte Carlo

Phase equilibrium of CO 2 decane liquids plays an important role in long-term behavior and storage of carbon dioxide in deep underground reservoirs and oil and gas wells. To this end, the Gibbs ensemble Monte Carlo (GEMC) simulation in the constant volume (canonical NVT ) ensemble were carried out to calculate the phase behavior of pure components viz- carbon dioxide, n-decane and argon. The Transferable Potential for Phase Equilibria (TraPPEUA) force fields was used to predict the vaporliquid equilibria coexistence behavior of decane and argon, while Elementary Physical Model 2 (EPM2 model) for carbon dioxide, were performed with constant volume GEMC simulation. From the results obtained, TraPPE-UA force field successfully studied the phase behavior of n -decane and argon, and by using rescaled EPM2 model the vapour-liquid equlibria of carbon dioxide (CO 2 ) was examined their miscibility (solubility) and the possibility of storing and tracking stored carbon dioxide in a reservoir (geological well).

scholarly journals Dynamic Monte Carlo algorithm for out-of-equilibrium processes in colloidal dispersions

2018 ◽  
Vol 20 (22) ◽  
pp. 15118-15127 ◽  
Author(s):  
Daniel Corbett ◽  
Alejandro Cuetos ◽  
Matthew Dennison ◽  
Alessandro Patti
Keyword(s):  
Phase Transition ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Nematic Phase ◽  
Colloidal Dispersions ◽  
Monte Carlo Algorithm ◽  
Equilibrium Processes ◽  
Dynamic Monte Carlo ◽  
Colloidal Rods

Field-induced isotropic-to-nematic phase transition of colloidal rods studied with Dynamic Monte Carlo simulations.

On the Influence of Uncertainties in Estimating Risk Aversion and Working Interest

1996 ◽  
Vol 14 (1) ◽  
pp. 13-45 ◽  
Author(s):  
J.A. MacKay ◽  
I. Lerche
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Input Parameter ◽  
Success Probability ◽  
Risk Tolerance ◽  
Relative Importance ◽  
Hyperbolic Tangent ◽  
Corporate Risk ◽  
Parameter Values

The influence of uncertainties in costs, value, success probability, risk tolerance and mandated working interest are evaluated for their impact on assessing probable ranges of uncertainty on risk adjusted value, RAV, using different models. The relative importance of different factors in contributing to the uncertainty in RAV in analyzed, as is the influence of different probability distributions for the intrinsic variables entering the RAV model formulae. Numerical illustrations indicate how the RAV probabilities depend not only on the model functions (Cozzolino, hyperbolic tangent) used to provide RAV estimates, but also on the intrinsic shapes of the probability distributions from which are drawn input parameter values for Monte Carlo simulations. In addition, a mandated range of working interest can be addressed as an extra variable contributing to the probabilistic range of RAV; while negative RAV values for a high-cost project can be used to assess the probable buy-out amount one should be prepared to pay depending on corporate risk philosophy. Also, the procedures illustrate how the relative contributions of scientific factors influence uncertainty of reserve assessments, allowing one to determine where to concentrate effort to improve the ranges of uncertainty.

scholarly journals Geological Model for Mauddud Reservoir Khabaz Oil Field

2021 ◽  
Vol 54 (1D) ◽  
pp. 29-42
Author(s):  
Rayan Ahmed
Keyword(s):  
Oil And Gas ◽  
Water Saturation ◽  
Oil Field ◽  
Carbonate Reservoirs ◽  
Geological Model ◽  
Petrophysical Properties ◽  
Geological Modeling ◽  
The North ◽  
Porosity And Permeability ◽  
Net To Gross

The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there are three basic steps to construct the geological model, starts with creating a structural, facies and property models. The reservoirs were divided into four zones depending on the variation of petrophysical properties (porosity and permeability). Nine wells that penetrate the Cretaceous Formation (Mauddud reservoir) are included to construct the geological model. Zone number three characterized as the most important due to it Is large thickness which is about 108 m and good petrophysical properties are about 13%, 55 md, 41% and 38% for porosity, permeability, water saturation and net to gross respectively. The initial oil and gas in place are evaluated to be about 981×106 STB and 400×109 SCF.

scholarly journals ACCEPTANCES AND AUTOCORRELATIONS IN THE HYBRID MONTE CARLO ALGORITHM

1992 ◽  
Vol 03 (01) ◽  
pp. 43-52 ◽  
Author(s):  
SOURENDU GUPTA
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Exponent ◽  
Quark Mass ◽  
Monte Carlo Algorithm ◽  
Xy Model ◽  
Acceptance Probability ◽  
Hybrid Monte Carlo ◽  
Lattice Size

The acceptance probability in Hybrid Monte Carlo simulations of QCD, in particular, its dependence on the lattice size, quark mass and the coupling, is discussed. Results on the tuning of parameters required in order to achieve low autocorrelations in the 2-d XY model are presented. In both phases of this model, the dynamical critical exponent is close to 2 for runs with trajectory lengths between 1 and 3 molecular dynamics units.

A Monte Carlo approach to determine the sensitivity of bedform analysis methods

2020 ◽  
Author(s):  
Julius Reich
Keyword(s):  
Monte Carlo ◽  
Evaluation Method ◽  
Input Parameter ◽  
Paraná River ◽  
Strong Impact ◽  
Data Set ◽  
Parana River ◽  
Input Parameters ◽  
The Individual ◽  
Echo Sounding

<p>There is a strong interaction between the appearance and dimensions of bedforms in rivers and the prevailing hydraulic and morphological conditions. The availability and mobility of sediments, in response to hydraulic variables like flow depth and velocity, determine the bedform characteristics. Vice versa, bedforms have a strong impact on the hydraulic conditions by exerting a flow resistance. Further on, with a thorough knowledge of the dimensions and migration velocities, predictions about sediment transport rates can be made.</p><p>Bedform geometries can be derived from multibeam echo sounding data. There are methods to discriminate several layers of superimposed bedforms and to identify the individual geometric attributes (length, height and shape). The calculated results, however, strongly depend on the setting of various input parameters. For choosing the values for these parameters there are mostly no theoretically sound criteria and the process itself is also strongly influenced by the individual experience of the researcher. If repeated several times by several researchers the analysis of the same data set would ultimately lead to different results. Only by means of a structured and traceable approach the level of inherent subjectivity and uncertainties can be reduced.</p><p>For the processing of multibeam echo sounding data we combined the existing software tools Bedforms ATM (Gutierrez et al., 2018) and RHENO BT (Frings et al., 2012) using an R-script. The concept of Bedforms ATM is based on a wavelet analysis in order to detect predominant bedform lengths. Applying this tool provides the rationale for deciding on the respective window sizes, which is a required input parameter for RHENO BT. The latter one is used to identify individual bedform geometries from longitudinal bedform profiles.</p><p>For estimating the sensitivity of all relevant input parameters to Bedforms ATM and Rheno BT an algorithm was developed in which a Monte Carlo-like simulation is performed. Assuming an individually chosen distribution function, random values are generated for each parameter. Multiple repetitions of the calculation with varying input parameters reveal the possible range of results. The algorithm has been tested on longitudinal profiles of Parana River in Argentina (Parsons et al., 2005) and an own data set of River Oder in Germany. The two case studies cover different ranges of bedform geometries, long and high bedforms characterize the morphology of the Parana River in contrast to much smaller and lower bedforms in the River Oder.</p><p>In the simulations carried out several input parameters turned out to be very sensitive. In some cases it can be shown that even slight variations lead to an increase in calculated mean bedform height of about 30 %. Further on, the type of statistical evaluation determines the robustness of the results. These uncertainties underline the need for comprehensive analyses before further processing in order to choose a reliable setting of input parameters and a suitable evaluation method.</p><p> </p><p> </p>

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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