An improved technique in porosity prediction: a neural network approach

Genetic reservoir characterization is important in developing, for a given petroleum reservoir, an improved understanding of the total amount and fluid flow properties of hydrocarbon reserves. Application of genetic concepts involves the classification of well log data into different lithofacies groups, followed by a facies-by-facies description of rock properties such as porosity and permeability. This work contrasts the genetic and nongenetic approaches in predicting porosity values of an oil well using backpropagation neural network methods. The performance of both methods are critically evaluated. A systematic technique to optimise the network configuration using weight visualization curves is proposed, thereby enabling the amount of training time to be significantly reduced. In the example problem, the genetic approach provides superior porosity estimates to that based on a nongenetic approach. >

[1]  J. L. Baldwin,et al.  Computer Emulation of Human Mental Processes: Application of Neural Network Simulators to Problems in Well Log Interpretation , 1989 .

[2]  Tamás D. Gedeon,et al.  Bimodal Distribution Removal , 1993, IWANN.

[3]  P. H. Nelson,et al.  Permeability Prediction From Well Logs Using Multiple Regression , 1986 .

[4]  B. Irie,et al.  Capabilities of three-layered perceptrons , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Jacques Pelissier-Combescure,et al.  Faciolog - Automatic Electrofacies Determination , 1982 .

[6]  S. Sakurai,et al.  Facies Discrimination And Permeability Estimation From Well Logs For The Endicott Field , 1988 .

[7]  Alberto Tesi,et al.  On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Maureen Caudill,et al.  Neural networks primer, part III , 1988 .

[9]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[10]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[11]  Alberto Prieto,et al.  New Trends in Neural Computation , 1993 .

[12]  J. M. Hutfilz,et al.  The Use of Routine and Special Core Analysis in Characterizing Brent Group Reservoirs, U.K. North Sea , 1992 .

[13]  Olivier Peyret,et al.  Automatic Determination of Lithology From Well Logs , 1987 .

[14]  Horst Bischof,et al.  Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..

[15]  A. Debra,et al.  Permeability Estimation Using a Neural Network: A Case Study from the Roberts Unit, Wasson field, Yoakum County, Texas , 1992 .

[16]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[17]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

[18]  Judith E. Dayhoff,et al.  Neural Network Architectures: An Introduction , 1989 .

[19]  O. Serra,et al.  The Contribution of Logging Data to Sedimentology and Stratigraphy , 1982 .

[20]  Xiao-Hu Yu,et al.  Can backpropagation error surface not have local minima , 1992, IEEE Trans. Neural Networks.

[21]  Jeffrey L. Baldwin,et al.  Application Of A Neural Network To The Problem Of Mineral Identification From Well Logs , 1990 .

[22]  C. L. Hearn,et al.  Geological Factors Influencing Reservoir Performance of the Hartzog Draw Field, Wyoming , 1983 .

[23]  Helge Hove Haldorsen,et al.  Challenges in Reservoir Characterization: GEOHORIZONS , 1993 .

[24]  D X Wang,et al.  Fast learning in a backpropagation algorithm with a sine-type thresholding function. , 1992, Applied optics.

[25]  Charles L. Karr,et al.  Determination of lithology from well logs using a neural network , 1992 .

[26]  Cheng Dang Zhou,et al.  Determining Reservoir Properties in Reservoir Studies Using a Fuzzy Neural Network , 1993 .

[27]  I. J. Taggart,et al.  A GENETIC APPROACH TO THE PREDICTION OF PETROPHYSICAL PROPERTIES , 1994 .

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.