Please enable JavaScript for this site to function properly.
OnePetro
  • Help
  • About us
  • Contact us
Menu
  • Home
  • Journals
  • Conferences
  • Log in / Register

Log in to your subscription

and
Advanced search Show search help
  • Full text
  • Author
  • Company/Institution
  • Publisher
  • Journal
  • Conference
Boolean operators
This OR that
This AND that
This NOT that
Must include "This" and "That"
This That
Must not include "That"
This -That
"This" is optional
This +That
Exact phrase "This That"
"This That"
Grouping
(this AND that) OR (that AND other)
Specifying fields
publisher:"Publisher Name"
author:(Smith OR Jones)

Use of Neural Networks for Prediction of Vapor/Liquid Equilibrium K-Values for Light-Hydrocarbon Mixtures

Authors
W.A. Habiballah (Texas A&M U.) | R.A. Startzman (Texas A&M U.) | M.A. Barrufet (Texas A&M U.)
DOI
https://doi.org/10.2118/28597-PA
Document ID
SPE-28597-PA
Publisher
Society of Petroleum Engineers
Source
SPE Reservoir Engineering
Volume
11
Issue
02
Publication Date
May 1996
Document Type
Journal Paper
Pages
121 - 126
Language
English
ISSN
0885-9248
Copyright
1996. Society of Petroleum Engineers
Disciplines
5.6.4 Drillstem/Well Testing, 4.1.5 Processing Equipment, 4.1.2 Separation and Treating, 5.5 Reservoir Simulation, 4.6 Natural Gas, 5.2.2 Fluid Modeling, Equations of State, 5.2.1 Phase Behavior and PVT Measurements, 4.3.4 Scale, 6.1.5 Human Resources, Competence and Training
Downloads
2 in the last 30 days
358 since 2007
Show more detail
View rights & permissions
SPE Member Price: USD 12.00
SPE Non-Member Price: USD 35.00

Summary

Equilibrium ratios play a fundamental role in the understanding of phase behavior of hydrocarbon mixtures. They are important in predicting compositional changes under varying temperatures and pressures conditions in reservoirs, surface separators, production and transportation facilities. In particular they are critical for reliable and successful compositional reservoir simulation. This paper presents a new approach for predicting K-values using Neural Networks (NN). The method is applied to binary and multicomponent mixtures, K-values prediction accuracy is in the order of the tradition methods. However, computing speed is significantly faster.

Introduction

Equilibrium rations, more commonly known as K-values, relate the vapor mole fractions (yi), to the liquid mole fraction (xi) of a component (i) in a mixture,

(1)

In a fluid mixture consisting of different chemical components, K-values are dependent on mixture pressure, temperature, and composition of the mixture.

There are a number of methods for predicting K-values, basically these methods compute K-values explicitly or iteratively. The explicit methods correlate K-values with components parameters (i.e. critical properties), mixtures parameters (i.e. convergence pressure). Iterative methods are based on the equation of state (EOS) and are, usually, tuned with binary interaction parameters.

Literature search and experience in the phase behavior of hydrocarbon systems, have shown that current explicit methods are not accurate because they neglect compositional affects. EOS approach requires extensive amount of computational time, may have convergence problems, and must be supplied with good binary interaction parameters. In compositional reservoir simulation where million of K-values are required, the method becomes time consuming and adds to the complexity of simulation studies making some of them impractical.

Neural Networks (NN) are emerging technology that seems to offer two advantages, fast computation and accuracy. The objective of this paper is to show the potential of using NN for predicting K-values. Different NN where trained using the Scaled Conjugate Gradient (SCG), and where used to predict the K-values for binary and multicomponent mixtures.

File Size  323 KBNumber of Pages   6
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
    • Issue 06
    • Issue 05
    • Issue 04
    • Issue 03
    • Issue 02
    • Issue 01
Show more

Other Resources

Looking for more? 

Some of the OnePetro partner societies have developed subject- specific wikis that may help.


 


PetroWiki was initially created from the seven volume  Petroleum Engineering Handbook (PEH) published by the  Society of Petroleum Engineers (SPE).








The SEG Wiki is a useful collection of information for working geophysicists, educators, and students in the field of geophysics. The initial content has been derived from : Robert E. Sheriff's Encyclopedic Dictionary of Applied Geophysics, fourth edition.

  • Home
  • Journals
  • Conferences
  • Copyright © SPE All rights reserved
  • About us
  • Contact us
  • Help
  • Terms of use
  • Publishers
  • Content Coverage
  • Privacy
  Administration log in