skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Journal Article · · Interpretation
 [1];  [2]
  1. West Virginia University, Department of Geology and Geography, West Virginia, Morgantown 26506, USA and The University of Texas at Austin, Bureau of Economic Geology, Texas, Austin 78713, USA.(corresponding author).
  2. West Virginia University, Department of Geology and Geography, West Virginia, Morgantown 26506, USA..
+ Show Author Affiliations

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Research Organization:
West Virginia Univ., Morgantown, WV (United States)
Sponsoring Organization:
USDOE Office of Policy and International Affairs (PO)
DOE Contract Number:
PI0000017
OSTI ID:
1613809
Journal Information:
Interpretation, Vol. 7, Issue 1; ISSN 2324-8858
Publisher:
Society of Exploration Geophysicists
Country of Publication:
United States
Language:
English

References (36)

A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs journal August 2010
Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study journal December 2010
Support-Vector Regression for Permeability Prediction in a Heterogeneous Reservoir: A Comparative Study journal June 2010
Fuzzy classifier based support vector regression framework for Poisson ratio determination journal September 2013
Robust linear programming discrimination of two linearly inseparable sets journal January 1992
A geologic history of the north-central Appalachians; Part 2, The Appalachian Basin from the Silurian through the Carboniferous journal September 1997
A geologic history of the north-central Appalachians; Part 1, Orogenesis from the Mesoproterozoic through the Taconic Orogeny journal June 1997
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences journal August 1998
Neural Networks and the Bias/Variance Dilemma journal January 1992
Evaluation of Acid Fracturing Treatments in Shale Formation journal September 2017
Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study journal July 2001
Reservoir oil viscosity determination using a rigorous approach journal January 2014
Determination of porosity and permeability in reservoir intervals by artificial neural network modelling, offshore Eastern Canada journal September 1997
Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks journal January 2006
A New Hybrid Forecasting Approach Applied to Hydrological Data: A Case Study on Precipitation in Northwestern China journal August 2016
Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea journal December 2005
What is a support vector machine? journal December 2006
Particle swarm optimization: An overview journal August 2007
Reservoir Characterization by a Combination of Fuzzy Logic and Genetic Algorithm journal February 2014
Influence of gravel on the propagation pattern of hydraulic fracture in the glutenite reservoir journal June 2018
A new model to evaluate two leak points in a gas pipeline journal October 2017
Investigation into the performance of oil and gas projects journal February 2017
A realistic and integrated model for evaluating oil sands development with Steam Assisted Gravity Drainage technology in Canada journal March 2018
Design of neural networks using genetic algorithm for the permeability estimation of the reservoir journal October 2007
Application of hybrid neural particle swarm optimization algorithm for prediction of MMP journal January 2014
What is noise? journal July 1998
Comparing support vector machines with Gaussian kernels to radial basis function classifiers journal January 1997
A tutorial on support vector regression journal August 2004
Least Squares Support Vector Machine Classifiers journal June 1999
Recurrent least squares support vector machines
  • Suykens, J. A. K.; Vandewalle, J.
  • IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 47, Issue 7 https://doi.org/10.1109/81.855471
journal July 2000
Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin journal March 2014
Fault diagnostics based on particle swarm optimisation and support vector machines journal May 2007
Porosity and Permeability Prediction Based on Computational Intelligences as Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in Southern Carbonate Reservoir of Iran journal May 2013
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis journal July 1999
Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction journal November 2016
Dew point pressure prediction based on mixed-kernels-function support vector machine in gas-condensate reservoir journal November 2018

Similar Records

Related Subjects

Geochemistry & Geophysics