Integration of Geochemistry with Fluid Property Data: A comprehensive fluids characterization workflow for understanding petroleum accumulations

2021 ◽  
Author(s):  
N. Marcano Balliache
Keyword(s):  
Fluid Property ◽  
Property Data

A Computer File Of Oil Reservoir Fluid Property Data

1978 ◽  
Vol 17 (02) ◽  
Author(s):  
D.H. Burger ◽  
R.A. Heidemann ◽  
K. Aziz Heidemann
Keyword(s):  
Oil Reservoir ◽  
Computer File ◽  
Fluid Property ◽  
Reservoir Fluid ◽  
Property Data

FLUID PROPERTY VARIATION ANALYSIS IN A HEAT EXCHANGER USING SUPERCRITICAL FLUIDS

2020 ◽  
Author(s):  
K H Jyothiprakash ◽  
Agniv Saha ◽  
Arihant Kumar Patawari ◽  
K. N. Seetharamu
Keyword(s):  
Heat Exchanger ◽  
Supercritical Fluids ◽  
Variation Analysis ◽  
Property Variation ◽  
Fluid Property

Design calculation model of oil fluid property parameter knowledge database for production logging

2020 ◽  
Vol 20 (3) ◽  
pp. 951-958
Author(s):  
Wenguang Song ◽  
Qiongqin Jiang
Keyword(s):  
Crude Oil ◽  
Expert Knowledge ◽  
Calculation Model ◽  
Physical Parameters ◽  
Base System ◽  
Design Calculation ◽  
Calculation Methods ◽  
Fluid Property ◽  
Knowledge Base System ◽  
Parameter Values

The fluid property parameter calculation affects the accuracy of the interpretation the accuracy, in the interpretation of the liquid production profile. Therefore, it is particularly important to accurately calculate the physical property parameter values, in the establishment of the fluid property parameter expert knowledge base system. The main physical parameters include the following calculation methods of the oil. The oil property parameter conversion formula mainly studies the formulas such as bubble point pressure, dissolved gas-oil ratio, crude oil volume coefficient, crude oil density, crude oil viscosity, and crude oil compression coefficient. Design expert knowledge base system, it is based on the calculation methods of these physical parameters. A computational fluid property parameter model is constructed by training production log sample data. Finally, the interactive and friendly product interpretation software model was developed in 9 wells’ data. The design calculation model can increase the accuracy to achieve 95% of oil fluid property parameter. Accurately calculate fluid property parameter values.

A Generalized Compositional Approach for Reservoir Simulation

1983 ◽  
Vol 23 (05) ◽  
pp. 727-742 ◽  
Author(s):  
Larry C. Young ◽  
Robert E. Stephenson
Keyword(s):  
Enhanced Recovery ◽  
Gas Condensate ◽  
Reservoir Modeling ◽  
Pressure Equation ◽  
Compositional Modeling ◽  
Fluid Property ◽  
Black Oil ◽  
Fluid Properties ◽  
Newton Raphson ◽  
Raphson Method

A procedure for solving compositional model equations is described. The procedure is based on the Newton Raphson iteration method. The equations and unknowns in the algorithm are ordered in such a way that different fluid property correlations can be accommodated leadily. Three different correlations have been implemented with the method. These include simplified correlations as well as a Redlich-Kwong equation of state (EOS). The example problems considered area conventional waterflood problem,displacement of oil by CO, andthe displacement of a gas condensate by nitrogen. These examples illustrate the utility of the different fluid-property correlations. The computing times reported are at least as low as for other methods that are specialized for a narrower class of problems. Introduction Black-oil models are used to study conventional recovery techniques in reservoirs for which fluid properties can be expressed as a function of pressure and bubble-point pressure. Compositional models are used when either the pressure. Compositional models are used when either the in-place or injected fluid causes fluid properties to be dependent on composition also. Examples of problems generally requiring compositional models are primary production or injection processes (such as primary production or injection processes (such as nitrogen injection) into gas condensate and volatile oil reservoirs and (2) enhanced recovery from oil reservoirs by CO or enriched gas injection. With deeper drilling, the frequency of gas condensate and volatile oil reservoir discoveries is increasing. The drive to increase domestic oil production has increased the importance of enhanced recovery by gas injection. These two factors suggest an increased need for compositional reservoir modeling. Conventional reservoir modeling is also likely to remain important for some time. In the past, two separate simulators have been developed and maintained for studying these two classes of problems. This result was dictated by the fact that compositional models have generally required substantially greater computing time than black-oil models. This paper describes a compositional modeling approach paper describes a compositional modeling approach useful for simulating both black-oil and compositional problems. The approach is based on the use of explicit problems. The approach is based on the use of explicit flow coefficients. For compositional modeling, two basic methods of solution have been proposed. We call these methods "Newton-Raphson" and "non-Newton-Raphson" methods. These methods differ in the manner in which a pressure equation is formed. In the Newton-Raphson method the iterative technique specifies how the pressure equation is formed. In the non-Newton-Raphson method, the composition dependence of certain ten-ns is neglected to form the pressure equation. With the non-Newton-Raphson pressure equation. With the non-Newton-Raphson methods, three to eight iterations have been reported per time step. Our experience with the Newton-Raphson method indicates that one to three iterations per tune step normally is sufficient. In the present study a Newton-Raphson iteration sequence is used. The calculations are organized in a manner which is both efficient and for which different fluid property descriptions can be accommodated readily. Early compositional simulators were based on K-values that were expressed as a function of pressure and convergence pressure. A number of potential difficulties are inherent in this approach. More recently, cubic equations of state such as the Redlich-Kwong, or Peng-Robinson appear to be more popular for the correlation Peng-Robinson appear to be more popular for the correlation of fluid properties. SPEJ p. 727

scholarly journals Thermal and Transport Properties of Molten Chloride Salts with Polarization Effect on Microstructure

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 746
Author(s):  
Jianfeng Lu ◽  
Senfeng Yang ◽  
Gechuanqi Pan ◽  
Jing Ding ◽  
Shule Liu ◽  
...  
Keyword(s):  
Heat Capacity ◽  
Polarization Effect ◽  
Chloride Salt ◽  
Coulombic Interaction ◽  
Molten Chloride ◽  
Self Diffusion ◽  
Local Structures ◽  
Property Data ◽  
Chloride Salts ◽  
And Storage

Molten chloride salt is recognized as a promising heat transfer and storage medium in concentrating solar power in recent years, but there is a serious lack for thermal property data of molten chloride salts. In this work, local structures and thermal properties for molten chloride salt—including NaCl, MgCl2, and ZnCl2—were precisely simulated by Born–Mayer–Huggins (BMH) potential in a rigid ion model (RIM) and a polarizable ion model (PIM). Compared with experimental data, distances between cations, densities, and heat capacities of molten chloride slats calculated from PIM agree remarkably better than those from RIM. The polarization effect brings an extra contribution to screen large repulsive Coulombic interaction of cation–cation, and then it makes shorter distance between cations, larger density and lower heat capacity. For NaCl, MgCl2, and ZnCl2, PIM simulation deviations of distances between cations are respectively 3.8%, 3.7%, and 0.3%. The deviations of density and heat capacity for NaCl between PIM simulation and experiments are only 0.6% and 2.2%, and those for MgCl2 and ZnCl2 are 0.7–10.7%. As the temperature rises, the distance between cations increases and the structure turns into loose state, so the density and thermal conductivity decrease, while the ionic self-diffusion coefficient increases, which also agree well with the experimental results.

scholarly journals A Comparative Study of Various Methods of Handling Missing Data in UNSODA

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 727
Author(s):  
Yingpeng Fu ◽  
Hongjian Liao ◽  
Longlong Lv
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Soil Property ◽  
Particle Density ◽  
Organic Matter Content ◽  
Nonparametric Tests ◽  
Matter Content ◽  
Support Vector ◽  
Soil Database ◽  
Property Data

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

Fitting Weibull Strength Data and Applying It to Stochastic Mechanical Design

1992 ◽  
Vol 114 (1) ◽  
pp. 35-41 ◽  
Author(s):  
C. R. Mischke
Keyword(s):  
Mechanical Design ◽  
Least Square ◽  
Stochastic Methods ◽  
Coordinate Transformations ◽  
Strength Data ◽  
The Third ◽  
Tension Tests ◽  
Property Data ◽  
Simple Tension ◽  
Best Fit

This is the second paper in a series relating to stochastic methods in mechanical design. The first is entitled, “Some Property Data and Corresponding Weibull Parameters for Stochastic Mechanical Design,” and the third, “Some Stochastic Mechanical Design Applications.” When data are sparse, many investigators prefer employing coordinate transformations to rectify the data string, and a least-square regression to seek the best fit. Such an approach introduces some bias, which the method presented here is intended to reduce. With mass-produced products, extensive testing can be carried out and prototypes built and evaluated. When production is small, material testing may be limited to simple tension tests or perhaps none at all. How should a designer proceed in order to achieve a reliability goal or to assess a design to see if the goal has been realized? The purpose of this paper is to show how sparse strength data can be reduced to distributional parameters with less bias and how such information can be used when designing to a reliability goal.

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