Review of neural network analyses performed in Croatian part of Pannonian basin (petroleum geology data)

1. Review of neural network analyses performed in Croatian part of Pannonian basin (petroleum geology data) T. Malvić, J. Velić & M. Cvetković (Croatia) 12th Hungarian geomathematical congress and 1st Croatian-Hungarian geomathematical congress Morahalom, 29-31 May, 2008

2. INTRODUCTION • Neural networks represent very strong tools for different prediction tasks in many sciences. • Petroleum geology, and geology overall, is one of the field where such networks can be very successful and relatively easy applied for different variable, like porosity, depth, lithology and saturation. • Up to now, the neural prediction is applied at the three Croatian fields (Figure 1). • The Okoli field (prediction of facies) in 2006; the Beničanci field(porosity) and the Kloštar field (lithology and saturation) in 2007.

3. Figure 1: Areas analyzed by neural networks in Croatia (in petroleum geology domain)

4. OKOLI FIELD 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. Methodology of neural networks using in reservoir facies prediction is presented through paper of MALVIĆ (2006) and basics given i ROSENBLATT, 1958; McCORMACK, 1991; RIEDMILLER & BRAUN, 1993. 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; Figure 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 3,4), 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%).

5. Figure 2: Structural map of c2 reservoir top with selected well's positions

6. Figure 3: 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 4: 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

7. BENIČANCI FIELD 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 (CHAMBERS & YARUS, 2002) by well porositites at the 14 well locations. It made the network training. The network was of the backpropagation type. It was fitted through 10000 iterations, searching for the lowest value of correlation between attribute(s) and porosities and the minimal convergence. Results are presented in the paper of MALVIĆ & PRSKALO (2007).

8. The best training was reached using all three attributes together, what indicated on tendency that neural networks like numerous inputs. Obtained results (Figure 7) were compared by previously interpolated geostatistical porosity maps (done by kriging and cokriging approaches;Figures 5 & 6). Relatively smooth map, and rarely reaching of measured porosity minimum and maximum, strongly indicates on conclusion that neural estimation is more precisely than previously interpolations (Figure 7). The cokriging approachincluded only reflection strength (derivation of amplitude) as secondary seismic source of information (compared by neural inputs of three attributes). On contrary, the neural approach favor using of all three attributes. In this case they are all in physical interconnection, but generally iIt alerts us on carefully and geologically meaningful selection of the network inputs for any reservoir analysis.

9. Figure 5: Kriging porosity map (color scale 4-10%) Figure 6: Cokriging porosity map (color scale 3-11%) Figure 7: Neural network porosity map (color scale 5-10%)

10. KLOŠTAR FIELD The filed is located in the Sava depression. The largest oil reserves are in Upper Miocene sandstones, i.e. in “I. (Lower Pontian age) and II. (Upper Pannonian age) sandstone series”. The basic principles of neural networks had been studied from ROSENBLATT (1958), ANDERSON & ROSENFELD (1989) and ZAHEDI (1993), and application on sandstones in diploma thesis of CVETKOVIĆ (2007). Geophysical borehole measurements were used as input data for the neural network analysis. Supervised neural networks (SNN) were trained in the sandstone series two wells (Klo-A and Klo-B). Firstly, input data where log data (curves SP, R16 and R64) used for prediction of lithology. Secondly, the neural network was used to predict saturation with hydrocarbons.

11. RESULTS: • Relatively small prediction error values and very good correspondence between predicted and real values was achieved. • This points out to great possibilities in neural network application on petroleum geology problems and in exploration. • Accuracy of prediction can additionally be heightened by adding more input data, primarily more data logs that are good at defining lithological composition and hydrocarbon saturation such as: Gamma Ray (GR), Compensated Neutron (CN) and Density (DEN) logs.

12. Figure 8: Lithology prediction in well Klo-B (1st case) Figure 9: Lithology prediction in well Klo-B (2nd case)

13. Figure 10: Saturation prediction in well Klo-A Figure 11: Saturation prediction in well Klo-B

14. CONCLUSIONS (Okoli field) • This is the first neural analysis of such type in hydrocarbon reservoir analysis in Croatia • Excellent correlation was obtained between predicted and true position of sandstone lithology (reservoir of Lower Pontian age in the Sava depression); • 2. On contrary, positions of predicted and true marlstones positions (in top and bottom) mostly do not correspond; • 3. 4. The best prediction (so called Face machine) is reached in relatively early training period. In B-1 well such prediction is observed in 2186th iteration, and in B-2 well in 7626th iteration; • 5. It means that in similar facies analyses in the Sava depression, it is not neccessary to use large iteration set (about 30000); • 6.The input dataset would need to be extended on other log curves that characterize lithology, porosity and saturation, like SP (spontaneous potential), CN (compensated neutron), DEN (density) and some other; • 7. The wished true prediction could reached 90 % (Face machine could be cinfigured with 90 % probability).

15. CONCLUSIONS (Beničanci field) • The neural network was selected as the tool for handling uncertainties of porosity distribution in breccia-conglomerate carbonate reservoir of the Badenian age; • 2. The lateral changes in averaged reservoir's porosities are influenced by the Middle Miocene depositional environments; • 3. The best porosity training results are obtained when all three seismic attributes (amplitude, frequency, phase) were used; • 4. The reached correlation is R2=0.987 and convergence criteria Se2=0.329; • 5. These values can slightly (a few percent) differs in every new training, what is consequence of stochastic (random sampling) is some processes of the network fitting; • 6. The result indicates that neural network very favor the numerous inputs, but also can be easily applied in the Beničanci field for porosity prediction.

16. CONCLUSIONS (Kloštar field) • Several artificial neural networks were trained with the tasks of: • Predicting lithology of Upper Pannonian sediments (“II sandstone series”) and Lower Pontian deposits (“I sandstone series”) as well as • Hydrocarbon saturation within these beds. • 2. Radial Basus Function and multi Layer Perceptron networks were used and excellent corresponding of true and predicted LITHOLOGICAL values was achieved; • 3. Also, same algorithm gave excellent corresponding relation for WATER SATURATION between real and predicted values; • 4. Acquired results show large potential of neural networks application in reservoir characterisation; • 5. Networks are also tools for acquiring quick results from well logsvertical and lateral correlation with the goal of reservoir variables prediction.

17. REFERENCES ANDERSON, J.A. and ROSENFELD, E. (1989): Neurocomputing: Foundations of Research. Cambridge, MA: MIT Press. CHAMBERS, R.L. & YARUS, J.M. (2002): Quantitative Use of Seismic Attributes for Reservoir Characterization. RECORDER, Canadian SEG, Vol. 27, pp. 14-25, June. CVETKOVIĆ, M. (2007): Petroleum geology use of neural networks on the example of reservoir in Kloštar field. University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, Graduate thesis,mentor Prof. Dr. J. Velić, 15. June 2007, 49 p. MALVIĆ, T. (2006): Clastic facies prediction using neural networks (Case study from Okoli field). Nafta, 57, 10, 415-431. MALVIĆ, T. and PRSKALO, S. (2007): Some benefits of the neural approach in porosity prediction (Case study from Beničanci field). Nafta, 58, 9, 455-467. McCORMACK, M.D. (1991): Neural Computing im Geophysics. The Leading Edge, 10/1, Society of Exploration Geophysicists. RIEDMILLER, M. and BRAUN, H. (1993): A direct adaptive method for faster backpropagation learning: The RProp algorithm. Proc. of the IEEE Intl. Conf. on Neural Networks, San Francisco, p. 586-591. ROSENBLATT, F. (1958): The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-408. ZAHEDI, F. (1993): Inteligent systems for business, expert systems with neural networks. Wodsworth publishing Inc.