Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Article Number 50
Number of page(s) 6
DOI https://doi.org/10.2516/ogst/2019021
Published online 23 May 2019
  • Wang G., Pu X.-L., Tao H.-Z. (2012) A support vector machine approach for the prediction of drilling fluid density at High temperature and high pressure, Pet. Sci. Technol. 30, 435–442. [CrossRef] [Google Scholar]
  • Karstad E., Aadnoy B. (1998) Density behavior of drilling fluids during high pressure high temperature drilling operations, IADC/SPE Asia Pacific Drilling Technology, 7–9 September, Jakarta, Indonesia. [Google Scholar]
  • Ram Babu D. (1998) Effect of PρT behavior of muds on loss/gain during high-temperature deep-well drilling, J. Pet. Sci. Eng. 20, 49–62. [Google Scholar]
  • Babu D. (1996) Effects of P-p-T behaviour of muds on static pressures during deep well drilling – Part 2: Static pressures, SPE Drill. Complet. 11, 91–97. [CrossRef] [Google Scholar]
  • Isambourg P., Anfinsen B., Marken C. (1996) Volumetric behavior of drilling muds at high pressure and high temperature, European Petroleum Conference, 22–24 October, Milan, Italy. [Google Scholar]
  • Harris O. (2004) Evaluation of equivalent circulating density of drilling fluids under high pressure-high temperature conditions, Petroleum and Geological Engineering, University of Oklahoma. [Google Scholar]
  • Al-Anazi A., Gates I. (2012) Support vector regression to predict porosity and permeability: Effect of sample size, Comput. Geosci. 39, 64–76. [Google Scholar]
  • Al-Anazi A., Gates I. (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs, Eng. Geol. 114, 267–277. [Google Scholar]
  • Al-Anazi A., Gates I. (2010) Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study, Comput. Geosci. 36, 1494–1503. [Google Scholar]
  • Kamari A., Hemmati-Sarapardeh A., Mirabbasi S.-M., Nikookar M., Mohammadi A.H. (2013) Prediction of sour gas compressibility factor using an intelligent approach, Fuel Process. Technol. 116, 209–216. [CrossRef] [Google Scholar]
  • Hemmati-Sarapardeh A., Alipour-Yeganeh-Marand R., Naseri A., Safiabadi A., Gharagheizi F., Ilani-Kashkouli P., Mohammadi A.H. (2013) Asphaltene precipitation due to natural depletion of reservoir: Determination using a SARA Fraction based intelligent model, Fluid Phase Equilib. 354, 177–184. [Google Scholar]
  • Farasat A., Shokrollahi A., Arabloo M., Gharagheizi F., Mohammadi A.H. (2013) Toward an intelligent approach for determination of saturation pressure of crude oil, Fuel Process. Technol. 115, 201–214. [CrossRef] [Google Scholar]
  • Kamari A., Gharagheizi F., Bahadori A., Mohammadi A.H., Zendehboudi S. (2014) Rigorous Modeling for Prediction of Barium Sulfate (Barite) Deposition in Oilfield Brines, Fluid Phase Equilib. 366, 117–126 [Google Scholar]
  • Kamari A., Khaksar-Manshad A., Gharagheizi F., Mohammadi A.H., Ashoori S. (2013) Robust model for the determination of wax deposition in oil systems, Ind. Eng. Chem. Res. 52, 15664–15672. [Google Scholar]
  • Osman E., Aggour M. (2003) Determination of drilling mud density change with pressure and temperature made simple and accurate by ANN, Middle East Oil Show, 9–12 June, Bahrain. [Google Scholar]
  • McMordie W., Bland R., Hauser J. (1982) Effect of temperature and pressure on the density of drilling fluids, SPE Annual Technical Conference and Exhibition, 26–29 September, New Orleans, Louisiana. [Google Scholar]
  • Demirdal B., Cunha J. (2007) Olefin based synthetic drilling fluids’ volumetric behavior under downhole conditions, Rocky Mountain Oil & Gas Technology Symposium, 16–18 April, Denver, Colorado, USA. [Google Scholar]
  • Sorelle R., Jardiolin R., Buckley P., Barrios J. (1982) Mathematical field model predicts downhole density changes in static drilling fluids, SPE Annual Technical Conference and Exhibition, 26–29 September, New Orleans, Louisiana. [Google Scholar]
  • Kutasov I. (1988) Empirical correlation determines downhole mud density, Oil Gas J. (United States) 86, 61–63. [Google Scholar]
  • Peters E., Chenevert M., Zhang C. (1990) A model for predicting the density of oil-base muds at high pressures and temperatures, SPE Drill. Eng. 5, 141–148. [CrossRef] [Google Scholar]
  • Hoberock L., Thomas D., Nickens H. (1982) Here’s how compressibility and temperature affect bottom-hole mud pressure, Oil Gas J. (United States) 80, 159–164. [Google Scholar]
  • El-Sebakhy E.A. (2009) Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme, J. Pet. Sci. Eng. 64, 25–34. [Google Scholar]
  • Chapelle O., Vapnik V., Bengio Y. (2002) Model selection for small sample regression, Mach. Learn. 48, 9–23. [Google Scholar]
  • Ghosh A., Chatterjee P. (2010) Prediction of cotton yarn properties using support vector machine, Fibers Polym. 11, 84–88. [CrossRef] [Google Scholar]
  • Tatar A., Shokrollahi A., Mesbah M., Rashid S., Arabloo M., Bahadori A. (2013) Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure, J. Nat. Gas Sci. Eng. 15, 82–92. [Google Scholar]
  • Rostami A., Kalantari-Meybodi M., Karimi M., Tatar A., Mohammadi A.H. (2018) Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 73, 22. [CrossRef] [Google Scholar]
  • Wilamowski B.M., Jaeger R.C. (1996) Implementation of RBF type networks by MLP networks, IEEE International Conference on Neural Networks, 3–6 June 1996, Washington, DC, USA 1670–1675. [Google Scholar]
  • Park J., Sandberg I.W. (1993) Approximation and radial-basis-function networks, Neural Comput 5, 305–316. [Google Scholar]
  • Meng Joo E., Shiqian W., Lu J., Hock Lye T. (2002) Face recognition with radial basis function (RBF) neural networks, IEEE Trans. Neural Netw. 13, 697–710. [CrossRef] [PubMed] [Google Scholar]
  • Chen S., Cowan C.F.N., Grant P.M. (1991) Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Netw. 2, 302–309. [CrossRef] [PubMed] [Google Scholar]
  • Orr M.J. (1996) Introduction to radial basis function networks, Center for Cognitive Science, University of Edinburgh, UK. [Google Scholar]
  • Sundararajan N., Saratchandran P., Lu Y. (1999) Radial basis function neural networks with sequential learning, World Scientific Publishing Co., Inc. [CrossRef] [Google Scholar]
  • Tatar A., Shokrollahi A., Mesbah M., Rashid S., Arabloo M., Bahadori A. (2013) Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure, J. Nat. Gas Sci. Eng. 15, 82–92. [Google Scholar]
  • Naseri S., Tatar A., Shokrollahi A. (2016) Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes, Flow Meas. Instrum. 48, 1–7. [Google Scholar]
  • Adib H., Kazerooni N., Falsafi A., Adhami M.A., Dehghan M., Golnari A. (2018) Prediction of sulfur content in propane and butane after gas purification on a treatment unit, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 73, 70. [CrossRef] [Google Scholar]
  • Ahmadi M.A., Shadizadeh S.R., Shah K., Bahadori A. (2018) An accurate model to predict drilling fluid density at wellbore conditions, Egyptian J. Pet. 27, 1–10. [CrossRef] [Google Scholar]
  • Ahmadi M.A. (2016) Toward reliable model for prediction drilling fluid density at wellbore conditions: A LSSVM model, Neurocomputing 211, 143–149. [Google Scholar]

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