Estimating the fracture gradient coefficient using neural networks for a field in the Middle East

Journal of Petroleum Science and Engineering

Volumn 49, Issue 3-4, 2005, Pages 193-211

  Malallah, Adel ^a   Nashawi, Ibrahim Sami ^a  

^a KUWAIT UNIVERSITY   (Kuwait)

Author keywords

Artificial neural network; Back propagation algorithm; Feed forward; Fracture gradient; Pore pressure gradient

Indexed keywords

BACKPROPAGATION ALGORITHM; FRACTURE GRADIENT; PORE PRESSURE GRADIENT;

ALGORITHMS; BACKPROPAGATION; MATRIX ALGEBRA; NEURAL NETWORKS; OIL FIELDS; OIL WELL DRILLING; PORE PRESSURE; ROCKS;

PETROLEUM RESERVOIRS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; FRACTURED MEDIUM; RESERVOIR CHARACTERIZATION;

ASIA; EURASIA; MIDDLE EAST;

EID: 29544444359     PISSN: 09204105     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.petrol.2005.05.006     Document Type: Article

References (43)

1. V.E. Althaus A new model for fracture gradient J. Can. Pet. Technol. 1977 98 (April-June)
2. M.A. Badri C. Sayers R.A. Hussein A. Graziano Pore Pressure Prediction Data Using Seismic Velocities and Log Data in the Offshore Nile delta, Egypt 2001 SPE Middle East Oil Show, Manama, Bahrain 68195
3. J.L. Baldwin Using a simulated bi-directional associative neural network memory with incomplete prototype memories to identify facies from intermittent logging data acquired in a siliciclastic depositional sequence: A case study, SPE22843 Proc. SPE Annu. Tech. Conf. Exhibition 1991 273-286
4. M.F. Biones G.A. Rojas J.A. Moreno O. Hidaigo Thermodynamic characterization of volatile hydrocarbon reservoirs by neural networks Proc. SPE Latin America and Caribbean Pet. Eng. Conf., Buenos Aires 1994 235-243
5. R.M. Breckels H.A.M. vanEekelen Relationship between horizontal stress and depth in sedimentary basins J. Pet. Technol. 1982 2191-2199
6. R.M. Brennan M.R. Annis A new fracture gradient prediction technique that shows good results in Gulf of Mexico abnormal pressure. SPE 13210 Annu. Tech. Conf. Exhib., Houston, TX 1984
7. S. Christman Offshore fracture gradients J. Pet. Technol. 1973 910-914
8. W.D. Constant A.T. Bourgoyne Jr. Fracture gradient prediction for offshore wells SPE Drill. Eng. 1988 136-140
9. J.C. Davis Statistics and Data Analysis in Geology 2nd edition 1986 John Wiley and Sons New York
10. A. Draou S.A. Osisanya New method for estimating of formation pressures and fracture gradients from well logs. SPE 63263 Annu. Tech. Conf. Exhib., Dallas, TX 2000
11. N. Dutta S. Gelinsky M. Reese M. Khan A new petrophysically constrained predrill pore pressure prediction method for deepwater Gulf of Mexico: A real time case study. SPE 71347 Annu. Tech. Conf. Exhib. New Orleans, LA 2001
12. B.A. Eaton Fracture gradient prediction and its application in oilfield operations J. Pet. Technol. 1969 1353-1360
13. B.A. Eaton T.L. Eaton Fracture gradient prediction for the new generation World Oil 1997 93-100
14. T. Finkbeiner M.D. Zoback In-Situ Stress and Pore Pressure in the South Eugene Island Field, Gulf of Mexico. SPE/ISRM 47212 Eurock, Trondheim, Norway 1998
15. G. Garcia W.W. Whitman Inversion of a lateral log using neural networks Proc. SPE Pet. Comp. Conf. 1992 295-304
16. R. Gharbi A. Elsharkawy Predicting the bubble-point pressure and formation-volume-factor of worldwide crude oil systems J. Pet. Sci. Technol. 21 1&2 2003 53-79
17. E. Harrison W.F. Kieschnick W.J. McGuirre The mechanics of fracture, induction and extension Trans. AIME 201 1954 252-263
18. P.W. Holbrook M.L. Hauck A Petrophysical-mechanical math model for real-time wellsite pore pressure/fracture gradient prediction. SPE 16666 Annu. Tech. Conf. Exhib., Dallas, TX 1987
19. P.W. Holbrook D.A. Maggiori R. Hensley Real-time pore pressure and fracture pressure determination in all sedimentary lithologies SPE Form. Eval. 1995 215-222 (Dec)
20. Z. Huang J. Shimeld M. Williamson J. Katsube Permeability prediction with artificial neural network modeling in the venture gas field offshore eastern Canada Geophysics 61 2 1996 422-436
21. M.K. Hubbert D.G. Willis Mechanics of hydraulic fracturing J. Pet. Technol. 1957 153-168
22. J.L. Jensen L.W. Lake Optimization of regression-based porosity-permeability predictions CWLS 10th Symposium, Calgary, Alberta, Canada 1985
23. I.R. Juniardi I. Ershaghi Complexities of using neural network in well test analysis of faulted reservoirs. SPE26106 SPE Annu. Western Reg. Meet., Anchorage 1993 711-721
24. Lee, S. H., 2000. Integrated reservoir characterization using nonparametric regression and multiscale markov random fields. Ph.D. Dissertation, Texas A and M University.
25. W.R. Matthews J. Kelly How to predict formation pressure and fracture gradient from electric and sonic logs Oil Gas J. 1967 92-106
26. W.S. McCulloch W. Pitts A logical calculus of ideas immanent in neural activity" Bull. Math. Biophys. 5 1943
27. S. Mohaghegh B. Balan S. Ameri Permeability determination from well log data. SPE30978 SPE Form. Eval. 12 3 1997 170-174
28. J.P. Mouchet A. Mitchell Abnormal pressure while drilling Elf Aquitaine Man. Tech. 2 1989 191
29. Nashawi, I.S., Malallah, A., 2005. Predicting the formation fracture gradient of Middle East reservoirs using non-parametric optimal transformations. SPE 92719. Middle East Oil and Gas Show, Manama, Bahrain.
30. R.M. Ostermeier J.H. Pelletier C.D. Winker J.W. Nicholson Trends in shallow sediment pore pressures-deepwater Gulf of Mexico, SPE/IADC 67772 Dril. Conf., Amsterdam, The Netherlands 2001
31. E.S. Pennebaker An engineering interpretation of seismic data. SPE 2165 Annu. Tech. Conf. Exhib., Houston, TX 1968
32. B.D. Ripley Modern Applied Statistics with S-Plus 1994 Springer-Verlag New York
33. B.D. Ripley Pattern Recognition and Neural Networks 1996 Cambridge University Press London
34. L.A. Rocha A.T. Bourgoyne Jr. A new simple method to estimate fracture pressure gradient SPE Drill. Complet. 1996 153-159
35. J. Sjöberg L. Ljung Overfitting, regularization, and searching for minimum in neural network Proceedings of the Fourth IFAC Int. Sym. on Adaptive Systems in Control Signal and Processing, Grenoble, France 1992 IFAC Oxford, U.K. 663-674
36. M. Smith N. Camichael I. Reid C. Bruce Lithofacies determination from wire-line log data using a distributed neural network Proc. IEEE Workshop Neural Signal Processing, Princeton, NJ 1991 482-492
37. V. Vuckovic Prediction of Fracture Gradients Offshore Australia. SPE 19468 Asia-Pacific Conf., Sydney, Australia 1989
38. N.R. Warpinski M.B. Smith Rock mechanics and fracture geometry. Recent advances in hydraulic fracturing SPE Monogr. 12 1979 57-80
39. W.A. Wendt S. Sakurai P.H. Nelson Permeability prediction from well logs using multiple regression In: L.W. Lake H.B Carroll Jr. (Eds.) Reservoir Characterization 1986 Academic Press, Inc. Orlando, FL
40. R. Wydrinski G.G. Ramos X. Weng M.L. Payne Pore pressure and stress prediction in geopressured formations, South China Sea, PRC. SPE 48883 Int. Conf. Exhib., Beijing, China 1998
41. G. Xue A. Datta-Gupta P.P. Valko T.A. Blasingame Optimal transformations for multiple regression: Application to permeability estimation from well logs SPE Form. Eval. 1997 85-94
42. C. Yoshida S. Ikeda B. Eaton An investigative study of recent technologies used for prediction, detection and evaluation of abnormal formation pressure in North and South America. IADC/SPE 36381 Asia Pacific Dril. Technol. Conf., Kuala Lumpur, Malaysia 1996
43. M. Zamora New method predicts gradient fracture Pet. Eng. Int. 1989 38-47 (Sept.)