Application of neural tools in geological data analyses. Dr. Tomislav Malvić, Grad. in Geol. INA-Industry of Oil Plc., E&P of Oil and Gas, Reservoir Engineering and Field Engineering Dept. (advisor)
Application of neural tools in geological data analyses
Dr. Tomislav Malvić, Grad. in Geol.
INA-Industry of Oil Plc., E&P of Oil and Gas, Reservoir Engineering and Field Engineering Dept. (advisor)
Faculty of Mining, Geology and Petroleum Engineering, Institute of Geology and Geological Engineering (visiting lecturer)
Visiting lecture for
IAMG student chapter in Szeged, Hungary
14th Nov 2008
Generally, neural networks can be described as:
Biological (human) andArtificial or simulated (computer algorithms based network).
Fig. 2: Artificial neurons (schematic)
Fig. 1: Biological (human) neurons
Fig. 3: The artificial neuron model
The Activation function – the value of output (U) is compared with condition necessary for hypothesis acceptance (t). The function is started only if this value is appropriate.
Fig. 4: Schematic organization of
neural network through the layers
The basic Equation 1 impies:
previously determined weighting coefficients,
Condition of hypothesis acceptance,
Number of layers,
Number of neurons in layer.
Coefficient estimation is BACK PROPAGATION process
(or backerror procedure).
Fig. 5: Adoption of weighting coefficientand error decreasing
Simple (basic) neuron architecture recognize inputs behaviour through finding linearity (it is perceptron concept).
Back-propagation network by backing error and adopting coefficient overcome this limitation using hidden layers. Backpropagation network is also called Multilayer Perceptron Network.
Such error is determined for each neuron, and applied for adopting weighting coefficient and activation value. It is learning (training) and validating of the network.
The weighting coefficient are calculated
through Equation 3 and 4.
Backpropagation (disadvantages) – the most used paradigm, but often characterised with long lasting training. Simple (basic) neuron architecture recognize inputs behaviour through finding linearity (it is perceptron concept). It resulted from the gradient descent method used in backprop.
This problem is often expressed in geophysical neural application. The very large dataset, and sending each channel (attribute, input) back can significantly decreased learning rate (slow processing) and paralyze the network.
Resilient Propagation Algorithm (rProp)– one of the often improvements of backprop. The main difference is using only of partial derivations in process of weighting coefficient adjustment. It is about 4-5 times faster than the standard backprop algorithm.
Radial Basis Function Algorithm (RBF) – is an artificial network that uses radial basis fnction as activation function. Very often it is applied in function approximation, time series prediction etc.
A radial basis function is a real-valued function whose value depends only on the distance from the origin or alternatively on the distance from some other point c, called a center.
Fig. 6: The Multi Layer Perceptron
(MLP) backprop network
Fig. 7: The Radial Basis Funcion (RBF) network
Fig.8: Areas analyzed by neural networks in Croatia
The neural analysis was performed using cVISION –
Neuro Genetic Solution commercial software.
The Okoli field, located in the Sava depression, is selected as the example for clastic facies prediction using neural network. The significant oil and gas reserves are proved in Lower Pontian sandstones.
The analysis is based on rProp algorithm.
The network is trained using log data (curves GR, R16", R64", PORE/T/W, SAND & SHALE) from two wells (code names B-1 & B-2).
The neural network was trained based on selected part of input data and registered lithology from c2 reservoir (as analytical target) of Lower Pontian age. Positions of facies (sand/marl sequences) were predicted.
The results indicate on over-trained network in the case of sandstone sequences prediction (Figures 10, 11), because the marl sequences in the top and the base are mostly replaced by sandstone.
The further neural facies modelling in the Sava depression need to be expanded with additional logs that characterised lithology and saturation (SP, CN, DEN).
Then, rPORP algorithm could be reached with more than 90% probability of true prediction (in presented analysis this value reached 82.1%).
Figure 9: Structural map of c2 reservoir top with selected well's positions
Figure 10: Relations of errors in periods of training (T), learning (L) and validation (V)
and position of Face and Best configurations (the symbols F, B in legend)
for B-1 well
Figure 11: Relations of errors in periods of training (T), learning (L) and validation (V) and position of Face and Best configurations (the symbols F, B in legend)
for B-2 well
The neural analysis was performed using NEURO3 – Neural Network Software.
It is freeware E&P Tools published by the National Energy Technology Laboratory (NETL), owned and operated by the U.S. Department of Energy (DOE) national laboratory system.
GENERAL LITHOLOGY AND NETWORK TYPE:
The reservoir is represented by carbonate breccia (and conglomerates) of Badenian age. Locally the thickness of entire reservoir sequence is locally more than 200 m.
The three seismic attributes were interpreted – amplitude, phase and frequencies making 3D seismic cube, averaged and correlated by well porosities at the 14 well locations.
The 14 seismic and porosity point data made the network training.
The network was of the backpropagation type. It was fitted through 10000 iterations, searching for the maximal value of correlation between attribute(s) and porosities and the minimal convergence.
Figure 12a: Kriging porosity map
(colour scale 4-10%)
Figure 12b: Cokriging porosity map
(colour scale 3-11%)
Figure 12c: Neural network porosity map
(colour scale 5-10%)
Neural analysis was done by package StatSoft STATISTICA 7
aError value ranges from 0 to 1, where 0 represents 100% success of prediction, i.e., no error.
LITHOLOGY PREDICTION (example in well “Klo-B”).
The better results are obtain by RBF network.
Figure 13: RBF network training
(II. sandstone series UP, I. sandstone series DOWN)
SATURATION PREDICTION (examples from Klo-A and Klo-B).
The better results are obtain in both wells by MLP network. .
Figure 13: MLP network training (both series are shown)
(Klo-A UP, Klo-B DOWN)
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