APLICACIÓN NO CONVENCIONAL DE REDES NEURONALES PARA PREDECIR PROPIEDADES PETROPHYSICA EN UN CUBO SISMICO

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1 Cersosimo, S., Ravazoli, C., Garcia-Martinez, R Non Classical Use o Neuronal Networks to Predict Petrophysica Propierties in a Seismic Cube Proceedings of II International Congress on Computer Science and Informatics (INFONOR-CHILE 2011) Pp ISBN APLICACIÓN NO CONVENCIONAL DE REDES NEURONALES PARA PREDECIR PROPIEDADES PETROPHYSICA EN UN CUBO SISMICO NON CLASSICAL USE OF NEURONAL NETWORKS TO PREDICT PETROPHYSICA PROPIERTIES IN A SEISMIC CUBE Dario Sergio Cersosimo 1, Claudia Ravazoli 2, Ramón García-Martínez 3 RESUMEN Las metodologías usadas en la industria para predecir propiedades petrofisicas utilizando los datos sismios y de pozo están basadas en algoritmos de redes neuronales y procesos de inversión de traza. El proceso de predicción está basada en el entrenamiento de redes neuronales. Las entradas son atributos sísmicos, propiedades petrofisicas y datos litologicos extraídos de una salida deseadae pozo. Este trabajo, propone utilizar como entrada de la red neuronal (RN) un conjunto de atributos sísmicos calculados de un horizonte interpretado previamente, es decir, en lugar de trabajar con el cubo sísmico, trabaja con los atributos sísmicos extraídos del horizonte sísmico, en la zona de interés. Los resultados, en este caso, son mejores que los producidos por abordajes convencionales. Palabras Claves: Predicción de propiedades petrofisicas, Redes Neuronales, Atributos Sísmicos, Datos litológicos, Cubo Sísmico. ABSTRACT The methodologies used in the industry to predict petrophysical properties through the seismic data and well data are based on neural network algorithms or trace inversion process. The prediction process is based on training of a neural network. The input are seismic attributes, petrophysical property, and lithologcal data extracted from the desired output well. This paper, proposes use as input of the neural network (NN) as the set of seismic attributes calculated from a previously interpreted horizon, i.e. instead of to work with the seismic cube, work with seismic attributes extracted from the seismic, horizon, in the zone of interest. The results, in this case, are better than those produced by conventional approaches. Keywords: Petrophysical properties prediction, Neural Networks, Seismic attributes, Lithological data, Seismic Cube INTRODUCCIÓN The principal idea of this study was to solve the prediction horizontal petrophysical properties of the rock, through the data well and seismic attributes calculated from interpreted horizons on a 3D seismic synthetic model [2]. Synthetic data has been worked coming from a synthetic geological model. The 3D synthetic seismic model was calculated with reflection coefficients convolved by a theoretical wavelet [4]. Once created the direct method was passed to solve the inverse method using as input the synthetic seismic cube calculated before. Thus the final product of this process was compared with synthetic geological model that led to the input cube. The process is based on the application of a back propagation neural network the inputs were seismic attributes associated with the target horizon. In this case, the inputs attributes that would be used for the neural network were calculated into a small window inside the wavelet Current methodologies, used in industry for predicting petrophysical properties through seismic data, [8] and well data, are based on NN algorithms or trace inversion process. Mainly related to the density, P impedance and 1 Programa de Doctorado, Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata. Paseo del Bosque s/n, La Plata, Buenos Aires, Argentina. sergio_cersosimo@jetband.com.ar 2 Grupo de Geofísica Aplicada, Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata. Paseo del Bosque s/n, La Plata, Buenos Aires, Argentina. 3 Grupo de Investigación en Sistemas de Información, Depto. Desarrollo Productivo y Tecnológico, Universidad Nacional de Lanús. 29 de Septiembre Remedios de Escalada, Lanús. Buenos Aires, Argentina. rgarcia@unla.edu.ar / rgm1960@yahoo.com

giving us an idea where to identify, on the seismic cube, the areas with the good chance to be drilled and finding the lithology variations and hydrocarbon.

DATA PROCESSING The data were generated from the interpreted horizon on synthetic data. In general terms, the data were extracted from two interpreted horizon.

2 S impedance. In the case of NN, the majority of work [6] [7] [11] [13] [9] are oriented at determining lithological variations, prediction of facies, petrophysical properties, structure determination, and others, giving us an idea where to identify, on the seismic cube, the areas with the good chance to be drilled and finding the lithology variations and hydrocarbon. In some papers [1] [5] [9] [10] [12] the calculation of any pseudo log is the variable to be predict in the whole seismic cube. DATA PROCESSING The data were generated from the interpreted horizon on synthetic data. In general terms, the data were extracted from two interpreted horizon. The synthetic cube was generated as follows: Step 1: Generation of a cube of speed from a velocity model generated based on a synthetic geological model previously determined. In this case generated a model of a meander with a thickness of 10 meters (see Figure 1). Step 2: Generation of a bucket of impedances from the velocities and densities. Step 3: Generation of a zero phase wavelet Riker and 25 Hrtz (see Figure 3). Step 4: Synthetic seismic generation (see Figure 2). Calculation of seismic attributes to be used in the neural network from the previously generated seismic. Step 5: Creation of an array of training between the attributes chosen and the desired profile (in this case speed). Step 6: Implement the training matrix to the whole seismic cube for the purpose of generating a pseudo-velocity cube. Step 7: Compare the result in well with the actual data. The initial velocity cube (see Figure 1) was performed using a geological approach, considering three layers, the first layer has 2950 m/s, the top is a 1000 meters and the base is a 3032 meters. The second layer start at 3032 and finish at 3043 meters with a velocity of a 2800 m / sec. In this layer a meandering channel has been developed and 10 meters thickness the velocity of this channel was 2650 m/seg. Figure 1. Velocity model associated with the channel. It can be seen the bend in yellow and his velocities below. Figure 2. Seismic section extracted from the seismic Figure 3. Riker wavelet of 25 Hrz. used for the

cube. generation of synthetic seismic cube. Figure 4. Synthetics velocity The top of the third layer is in 3044 meters to 4000 meters with a velocity of 3100 m / sec.

3 cube. generation of synthetic seismic cube. Figure 4. Synthetics velocity The top of the third layer is in 3044 meters to 4000 meters with a velocity of 3100 m / sec. A vertical velocity slide can be seen in Figure 4. With the velocity cube resolved, the density cube is solved in this case Garner equation was used, the Garner equation relates the velocity with the density. And, with the density cube, the velocities and the geological model given by the layers described, it has been proceeded to generate the synthetic seismic cube. This as in all the cases, the synthetic cube was generated by the convolution between the reflection coefficients given by the contrasts of acoustic impedance and the 25 htrz Riker wavelet, previously generated (see Figure 2). Once the synthetic seismic cube was done, it has been proceeded to interpret a horizon associated with the study area. In this way and across the interpreted horizon, the attributes calculation was performed. 11 random attributes are used (see Figure 5). The time window used to calculate the attribute was on the basis of the seismic wavelet. That is, the window length was from the zero crossing from negative to zero crossing to positive (see Figure 6). Figure 5. Synthetic seismic cube. Showing the development of the amplitudes calculated in a 20 mseq window

Amplitude, Amplitude of the cosine of the phase, frequency, Average frequency, Derivative, Envelope, and Amplitude of the phase.

4 Figure 6. Details of the synthetic seismic data, window used to calculate seismic attributes through the interpreted horizon The inputs used in the network were: X Coordinate, Y Coordinate, Inline, Crossline, Amplitude, Amplitude of the cosine of the phase, frequency, Average frequency, Derivative, Envelope, and Amplitude of the phase. The desired output was the velocity of 16 points selected from the model, called pseudo wells. These points can be identified by the ordered pair (Inline, Crossline). Table 1 represents the training matrix used. Table 1. Training matrix Used Pseudo- Wells As it can be seen, it has 16 velocity laws to train the network, generated from the pseudo wells seen in the previous figure. In this case the value of each of the 11 attributes selected is extracted and then completed in Table 1. The following charts (Figure 8 and Figure 9) correspond to some of the attributes used. The choice of these attributes was totally random. Figure 7. Pseudo wells distribution Figure 8 shows the calculated average frequency attribute in the same window. It is important to remember that choosing the time window for the calculation of attributes is based on the zero crossing from negative to positive and negative from positive.

But a better solution to the problem, was, choosing a window of time according to the thickness of the geological events that are identified in wells.

The target perfectly well can be seen. Simply, it is possible to observe the impact of the velocity changes in the amplitudes, i.e. it is probable that in many cases, the attributes shows us the

5 But a better solution to the problem, was, choosing a window of time according to the thickness of the geological events that are identified in wells. Figure 9 represents the "amplitude of the cosine of the phase" of seismic data measured from the horizon interpreted calculated within the time window identified by Figure 6. The target perfectly well can be seen. Simply, it is possible to observe the impact of the velocity changes in the amplitudes, i.e. it is probable that in many cases, the attributes shows us the event wanted to be characterized, but the point of this, is the scale value that the neural network will give us at the final product. Figure 10 represent the final process of data generated with the neural network. Figure 8. Average frequency Figure 9. Amplitude attributes

6 Figure 10. Final processing of the neural network. It can see that the neuronal network also fits with the velocity CONCLUSIONS A solution has been presented based on a nonconventional use of neural net to solve the problematic associated with the horizontal prediction of a petrophysical property and log prediction through seismic attributes calculated from previously interpreted horizons on a 3D synthetically seismic model and well logs. It can be concluded that the method described is applicable because: It has predictive power in terms of calibration values and function of the reliability of seismic amplitudes It has predictive power in terms of trend prediction The trend predicted data depend to the variations in the attributes It was noted that the input data for training and data output array must be of the same order of magnitude The input attributes that were used to the neural net, were calculated within a window which involves the wavelet associated with the seismic event. In this particular case the proposed methodology allows obtain a good fit with the geological initial model. ACKNOWLEDGMENT This research has been partially funded by National University of Lanus Research Project 33A105. REFERENCES 1. Banchs, R., Michelena, R. (2002). From 3D seismic atrributes to pseudo-well-log volumes using neural networks: Practical considerations. The Leading Edge 21, 996. ISSN: X. 2. Cersósimo, S., Ravazoli, C., García-Martínez, R Inversión Sísmica de un Modelo Teórico Calculado Sobre un Horizonte Sísmico Utilizando Redes Neuronales. Proceedings de la 3ª Convención de la Asociación Colombiana de Geólogos y Geofísicos Petroleros. 3. Cersósimo, S., Ravazoli, C., García-Martínez, R Inversión Sísmica de un Modelo Teórico Calculado Sobre un Horizonte Sísmico Utilizando Redes Neuronales. Boletín de Informaciones Petroleras 1(1): Cersosimo, S., Ravazzoli, C., García-Martínez, R., Identification of Velocity Variations in a Seismic Cube Using Neural Networks. IFIP Series, 218: ISBN:

7 5. Chopra S., Pruden, D. (2003). Multiattribute seismic analysis on AVO-derived parameters A case study. The Leading Edge 22, 998. ISSN: X. 6. Coléou, T., Poupon, M., Azbel, K. (2003). Interpreter's Corner Unsupervised seismic facies classification: A review and comparison of techniques and implementation. The Leading Edge 22, 942. ISSN: X. 7. de Rooij, M., Tingdahl. K. (2002). Metaattributes the key to multivolume, multiattribute interpretation. The Leading Edge 21, ISSN: X. 8. Hart, B. (2002) Validating seismic attribute studies: Beyond statistics. The Leading Edge 21, ISSN: X. 9. Lau, A. Gonzalez, A., Mallick, S., Gillespie, D. (2002). Waveform gather inversion and attributeguided interpolation: A two-step approach to log prediction. The Leading Edge 21, Singh, V., Srivastava, A., Tiwary, D., Painuly, P., Chandra, M. (2007). Neural networks and their applications in lithostratigraphic interpretation of seismic data for reservoir characterization. The Leading Edge 26, ISSN: X. 11. Strecker, U., Uden, R. (2002). Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps. The Leading Edge 21, ISSN: X. 12. Sun, Q., Eissa, M., Castagna,J., Cersosimo, S., Sun, S., Decker, C. (2001). Porosity from artificial neural network inversion for Bermejo Field, Ecuador. SEG Expanded Abstracts 20, 734; doi: / West, B., May, S., Eastwood, J., Rossen, C. (2002). Interactive seismic facies classification using textural attributes and neural networks. The Leading Edge 21, ISSN: X.

Identification of Velocity Variations in a Seismic Cube Using Neural Networks

Identification of Velocity Variations in a Seismic Cube Using Neural Networks Dario Sergio Cersósimo, Claudia Ravazoli, Ramón García-Martínez PhD Program & Geophisic Group of the Astronomic & Geophisic