Neural Inversion Technology for reservoir property prediction from seismic data

Transcription

1 Original article published in Russian in Nefteservice, March 2009 Neural Inversion Technology for reservoir property prediction from seismic data Malyarova Tatyana, Kopenkin Roman, Paradigm At the software market for seismic data interpretation and geological models creation more & more implementations appear which use the elements of artificial intelligence. The advance technology of one of the leading player in geophysical service area Paradigm - allows accurate calculation of parameters & reservoir properties when there is small number of wells and partial information about acoustic properties of the environment. Our brain is the most powerful supercomputer. It has enormous potential of learning and remembering things, correcting mistakes, and finding solutions. The human brain s complexity and many-sided nature is so great that modulating its processes with a help of usual computers is a challenging task. Nevertheless, over time technological progress has enabled artificial neuron networks (ANN) invention calculation systems, which convert the information as it is made by our brain. Today ANN are successfully used in different areas where the problems of images recognition & classification should be solved, for functions approximation, forecasts, optimization & management. Such networks are used for different tasks solution in science, industry, medicine, etc. The artificial intellect is widely used by geophysical methods. Various algorithms of neural networks are used for: Seismic trace editing and first break selection; Reconstruction of well logs or their parts; Log data clustering and electrofacies prediction; Seismic data classifications and seismic facies prediction; Transformation of seismic attributes into petrophysical properties. Paradigm offers technology, such as Paradigm Stratimagic, Paradigm Geolog (Facimage), Paradigm Echos and Paradigm Vanguard that enables completion of those tasks. The majority of these technologies together with the whole range of other methodical approaches are targeted at reservoir properties determination. Inversion for reservoir properties prediction At the present time, the amplitude inversion is widely applied to the reservoir characterization prediction. Seismic and logging data precision allows reservoir geoscientists to determine acoustic characteristics of 5-7 meters thick layers with confidence. The majority of inversion algorithms require a background model in order to the low-frequency components and constraints for the inversion optimization process. Construction of the detailed background model is especially important in complex seismic and geological conditions with lithology and facies heterogeneity and considerable variability of reservoir characteristics. However, sonic and density log data that are required for background model creation, are not always available, or there is not enough information on some wells. In these cases it is necessary to perform the

2 direct transformation of seismic data into the petrophysical property volume, for example, porosity, density, or to the pseudo log volume (SP, GR, etc.). The neural inversion process is realized in several stages: - Data analysis and modeling; - Training and determining the functional transformation operator; - Functional transformation of the whole volume; - Interpretation of the results. The most important stage enabling neural network inversion efficiency and expediency is preliminary data analysis and estimation of layer parameters (thickness, porosity, density, etc.) influence on a seismic response. The studied reservoir should be thoroughly described over the certain area by petrophysical parameters at the wells that have revealed different deposition patterns. Certain lithological and petrophysical indicators should have influence on seismic response. If all conditions are met, one can expect a good correlation between petrophysical parameters and seismic data that will enable the creation of a soft petrophysical property volume best fitted to the real geological model. Seismic to well calibration must be performed before Neural Inversion is applied. Seismic events and log data should be correctly aligned. The neural network is trained to find a relationship between the input seismic and log data. Unlike conventional acoustic impedance inversion, which is a deterministic inversion process that assumes the convolution model, Neural Network Inversion does not assume a convolution model. Instead, it uses a statistical approach which tries to estimate this relationship at the well location and apply it to all other locations. Since functional transformation between the seismic trace and the log property might be complex, using additional information can make this procedure more robust. Pre-calculated attributes, such as acoustic impedance, AVO attributes, dip or azimuth, and complex trace attributes can be calculated on-the-fly from the primary seismic dataset. The operator is multi-dimensional and non-linear. The more attributes, the higher the dimension of the operator and the more flexible it is. When using additional data, PCA (Principal Component Analysis) can be used to reduce redundancy. For quality control purposes, the actual output at well location is saved as a log curve in the database. It can then be compared with input data. Based on this procedure, one can make a proper decision whether the whole volume needs to be processed or the training parameters should be changed. After the training is completed, an operator is calculated and saved. One can then perform functional transformation of the whole seismic volume and calculate petrophysical properties volumes. Case study of neuronal inversion

3 The results of neuronal inversion for prediction of reservoir properties of one of the layers in the case study (license block) area in the south-eastern part of Western Siberia are presented in this article. The SP curve is the most informative parameter for lower cretaceous deposits in these real geological environments. This curve not only allows separate production layers (B10) in the reservoir non reservoir zones, but also estimates the quality of reservoir. Effective thicknesses of production layers are vary from 8 to 22 meters. There are bright spots and weak amplitude / interferential anomalies observed on the seismic (fig.1). Fig.1. Analyzed interval in 3D seismic The modeling which was carried out using sonic and density logs shows that the layer s petrophysical parameter changes are reflected in seismic data. The amplitude maximum became weaker with increasing shalinness of the layer. This shows the necessity of applying neuronal inversion applying forto the targeting interval. Four of five wells that penetrated the B10 layer were used for training (one of the wells was excluded for the final quality control of results). SP curves were smoothed initially in a 5-7 meters window for removing the high frequency signal component and to reduce the vertical resolution of the seismic data. Original seismic cube and also additional complex seismic attributes (Signal Envelope, Cosine of Instantaneous Phase, Amplitude-Weighted Instantaneous Frequency, and Differentiation) were used for the training process. The seismic cube was transformed to pseudo-sp cube after the determination of inversion operator (fig. 2).

4 Quality evaluation of the pseudo-sp curves from the derived cube shows a good correlation in shape and magnitude all well positions including the well location excluded from the neuron training. Fig.2. Random section of the pseudo-sp cube Using SP values as reservoir cut off criteria, the results were analyzed in the VoxelGeo system using transparency technology (fig.3). The geological object was detected automatically, and a time thickness map of minimum SP distribution was calculated (fig.4). A possible line of sand layer (B10) replacement of the shale facies, which corresponds possibly to palaeo shelf edge, was mapped in the north-west of study. There are three specific zones of separation here. Fig.3. Separation of low amplitude SP up to cut off values The first zone (I), which elongates from south-west to north-east and includes well 1 is interpreted as a good reservoir property zone, corresponds to the B10 top. The south-east zone (III) has a poor quality reservoir signatures and a significant decrease of effective thickness of B10, which is confirmed by well 5 drilling results. Zone II is penetrated by 2, 3 and 4 wells and shows high reservoir effective thickness and sand/shale interbedding (some deterioration of B10 quality compare to zone I detects here).

5 Finally, more perspective positions for the new exploration wells were chosen, using also structure closure and water oil contact (WOC) position. I 1 II 3 2 III 5 4 Fig.4. Low pseudo-sp values time thickness map Thus neuron inversion allows detail of the geological model of B10 layer, predicts effective thicknesses and lines of facial changes, and also finds zones of improved reservoirs despite the lack of well and acoustic data. Conclusion Neuron network inversion has some advantages in comparison with traditional acoustic inversion. This technique needs less input data. There is only one amplitude cube mandatory for transformation. The result is not limited by acoustic or elastic impedance calculation, but provides the opportunity to generate informative cubes of petrophysical parameters, if a correlation exists between density, porosity, VSH etc. and seismic data. Using complex multidimensional and non linear relations in analysis establishes the prerequisites for the successful prediction of reservoir properties.