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Articles

Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

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Pages 675-686 | Received 29 Apr 2013, Accepted 01 Jun 2013, Published online: 11 Jul 2013

References

  • Al-Fattah, S. M., & Startzman, R. A. (2001). Predicting natural gas production using artificial neural network. Paper SPE 68593 presented at SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, TX, April 2–3. doi:10.2118/68593
  • Ali, J. K. (1994). Neural networks: A new tool for the petroleum industry? Paper SPE 27561 presented at European Petroleum Computer Conference held in Aberdeen, UK, March 15–17. doi:10.2118/27561
  • Baldwin , B. and Butler , C. O. 1985 . Compaction curves . AAPG Bulletin , 83 : 622 – 626 .
  • Bingham , M. G. 1965 . A new approach to interpreting rock drillability , New York , NY : The Petroleum Publishing Company . p. 93
  • Centilmen, A., Ertekin, T., & Grader, A. S. (1999). Applications of neural-networks in multi-well field development. SPE 56624. Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, October. doi:10.2118/56624
  • Demuth, H., & Beale, M. (1998). Neural network toolbox for use with MATLAB. User’s Guide, Fifth Printing (Version 3). USA: Mathworks, Inc.
  • Doraisamy, H., Ertekin, T., & Grader, A. S. (1998). Key parameters controlling the performance of neuron- simulation applications in field development. SPE 51079 presented at SPE eastern Regional Meeting, Pittsburgh, Pennsylvania, November 9–11. doi:10.2118/51079
  • Dutta, N., Mukerji, T., Prasad, M., & Dvorkin, J. (2002). Seismic estimation and detection of overpressure. Part II: Field applications. CSEG Recorder, 27, 58–73.
  • Eaton , B. A. 1976 . Graphical method predicts geopressures worldwide . World Oil , 183 : 100 – 104 .
  • Eaton, B. A., & Eaton, T. L. (1997). Fracture gradient prediction for the new generation. World Oil, 218, pp. 93–94, 96–100.
  • Ebrom, D., Heppard, P., Mueller, M., & Thomsen, L. (2003). Pore pressure prediction from S-wave, C-wave, and P-wave velocities, Paper presented at the 73rd Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, Dallas, TX, October 26–31.
  • Hottman , C. E. and Johnson , R. K. 1965 . Estimation of formation pressures from log-derived shale properties . Journal of Petroleum Technology , 17 : 717 – 722 .
  • Huang , Z. , Shimeld , J. , Williamson , M. and Katsube , J. 1996 . Permeability prediction with artificial neural network modeling in the venture gas field offshore eastern, Canada . Geophysics , 61 : 422 – 436 .
  • Hubbert , M. and Rubey , W. W. 1959 . Role of fluid pressure in mechanics of overthrust faulting . Geological Society of America Bulletin , 70 : 167 – 206 .
  • Jorden , J. R. and Shirley , O. J. 1966 . Application of drilling and performance data to overpressure detection . Journal of Petroleum Technology , 18 : 1387 – 1394 .
  • Keshavarzi, R., Jahanbakhshi, R., & Rashidi, M. 2011. Predicting formation fracture gradient in oil and gas wells: A neural network approach. Papers presented at the 45th ARMA Symposium, San Francisco, June 26–29, 2011.
  • Lee, S. H. (2000). Integrated reservoir characterization using nonparametric regression and multiscale markov random fields (PhD dissertation). Texas A and M University, USA.
  • Mohaghegh, S. (2000). Virtual-intelligence applications in petroleum engineering. Parts 1 – artificial neural networks. Journal of Petroleum Technology, 64-73 (Distinguished Author Series).
  • Mukerji, T., Dutta, N., Prasad M., & Dvorkin, J. (2004). Seismic detection and estimation. Part I: The rock physics basis. CSEG Recorder, 27, 34–57.
  • Rehm, W. A., & McClendon, R. (1971). Measurements of formation pressure from drilling data. SPE 3601. Paper presented at Fall Meeting of the Society of Petroleum Engineers of AIME, New Orleans, Louisiana, October 3–6, 1971.
  • Rieke, H. H., & Chillingarian, G. V. (1974). Compaction of argillaceous sediments. (Developments in Sedimentology, 16). Amsterdam: Elsevier. p. 424.
  • Ripley , B. D. 1994 . Modern applied statistics with S-Plus , New York , NY : Springer-Verlag . p. 462
  • Ripley , B. D. 1996 . Pattern recognition and neural networks , London : Cambridge University Press . p. 403
  • Sadiq, T., & Gharbi R. (1998). Prediction of frictional drag and slack of transmission in horizontal wells using neural networks. Paper SPE 51083 presented at SPE Eastern Regional Meeting, Pittsburgh, PA, USA, November 9–11. doi:10.2118/51083
  • Shir Mohammadi, N. H. (1980). Geological study of Asmari reservoir in M field (Report No. P-3703). Iran: National Iranian Oil Company.
  • Swarbrick, R. E. (2002). Challenges of porosity-based pore pressure prediction. Canadian Society of Exploration Geophysicists Recorder, 27, 74–77.
  • Terzaghi, K., & Peck, R. P. (1968). Soil mechanics in engineering practice. New York, NY: John Wiley and Sons.
  • Yoshida, Ch., lkeda, Sh., & Eaton, B. A. (1996). An investigative study of recent technologies used for prediction, evaluation of abnormal formation pressure and fracture pressure America. In IADC/SPE Asia Pacific Drilling Technology Conference Kuala Lumpor, Malaysia, September 9–11. doi:10.2118/36381

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