SEISMIC RESERVOIR CHARACTERIZATION WITH LIMITED WELL CONTROL Tanja Oldenziel 1, Fred Aminzadeh 2, Paul de Groot 1, and Sigfrido Nielsen 3 1 De Groot-Bril Earth Sciences BV Boulevard 1945 # 24, 7511 AE Enschede, The Neetherlands 2 De Groot-Bril Earth Sciences 2500 Tanglewilde, Suite 120, Houston Texas 77063, USA 3 GeoInfo SRL, 25 de Mayo 168 9º piso, C1002ABD Buenos Aires, Argentina Keywords Seismic reservoir characterization with limited well control Abstract In this paper, we present a reservoir characterization workflow for fields with limited well control. An onshore German gasfield case study is presented to discuss different techniques. Central to all techniques is the use of a set of 300 simulated pseudo-wells that was created to extend the well data base of six real wells. The pseudowells are simulated using statistical input derived from the real wells and geological knowledge supplied in the form of rules and constraints. The simulated set is representative of the expected variations in geology, petrophysics and seismic response in the study area. In the first technique seismic data is analysed by segmenting seismic waveforms around the reservoir level using an unsupervised neural network. Subsequently, the seismic character of each segment is quantified in terms of the reservoir properties porosity and N*Phi using the pseudo-wells. In the second technique seismic and impedance cubes are inverted to a porosity volume using a supervised neural network. The neural network is trained on synthetic traces of the pseudo-wells. The real wells are used as blind test wells and indicate the high quality of the porosity inversion. The pseudo-wells are essential to the success of this study. Without these we do not have enough statistics to analyse the waveform segmentation maps. Neither would it be possible to produce a realistic porosity volume. The real wells in the area are all drilled on amplitude character and recorded similar porosities. Low and high porosities, which are known to exist in the geologial setting are not represented in the real well data base, but are represented in the simulated set. Introduction The example is from northwest Germany where gas is present in Rotliegend (Permian) aeolian sandstones. Two 3D seismic volumes were available: zero-phase reflectivity and acoustic impedance. Six wells fall inside the study area. These were used to derive the statistical variations needed by the pseudo-well simulator and served as blind test locations to validate the predictions. In the workflow the stratigraphy, logs, and relevant well data are fully integrated according to a user-defined integration framework. The framework defines the hierarchy of the stratigraphic units and also what information can be stored at each individual unit. A well (real or simulated) therefore consists of layers with stratigraphic identification and attached petrophysical data. The integrated well data is linked to the seismic data, after which interrelationships between the various datatypes can be studied at the hierarchical scale levels defined by the integration framework. The inter-relationships are then used to predict the same features from the factual seismic data. The aim of seismic reservoir characterization is to relate seismic measurements to relevant geological and petrophysical reservoir properties. The process involves analyzing complex relationships between huge amounts of data originating from different sources, acquired at different scale levels and accuracies. In the last decade artificial neural networks have been used successfully by many workers to aid in the process of finding these complex relationships. In this study two types of seismic pattern recognition techniques have been used: unsupervised and supervised. The main difference between supervised and unsupervised approaches lies in the amount of a-priori knowledge, which is
supplied. Below a more detailed discussion on neural networks will follow. Neural networks enable computer systems to imitate some desirable brain properties. Various types of networks have been applied successfully in a variety of scientific and technological fields. Examples are applications in industrial process modeling and control, ecological and biological modeling, sociological and economical sciences, as well as medicine (Kavli, 1992). Within the exploration and production world, neural network technology is routinely applied to geologic log analysis (Doveton, 1994, Nikravesh and Aminzadeh, 2001) and seismic attribute analysis (e.g. Schultz, 1994, de Groot, 1998). Basically, two learning approaches can be recognized in neural network modeling: supervised and unsupervised. The supervised approach requires the existence of a representative dataset. The network learns by feeding it examples from the representative dataset (the training set). The neural network then learns how the input data is related to the desired output. The supervised approach is a form of non-linear, multivariate regression that is used to quantify or classify data. Examples of quantification are networks that predict, from the seismic response, properties such as porosity or porevolume. Examples of classification are: classifying seismic waveforms into classes representing a specific fluid-fill, or a lithology. Popular supervised learning networks are: Multi-Layer Perceptrons and Radial Basis Functions networks (e.g. de Groot, 1995) or Hybrid Neural Networks (e.g. Aminzadeh, et al, 2000) In the unsupervised approach the aim is to find structure in the data themselves, without imposing an a-priori conclusion. Unsupervised learning is used for data segmentation, i.e. data clustering. The resulting segments (e.g. clusters of similar seismic waveforms at the reservoir level) remain to be interpreted. Popular networks that use unsupervised learning are the Unsupervised Vector Quantiser (de Groot, 1995) and Kohonen Feature Maps (e.g. Lippmann, 1989). Neural networks are simply a way of mapping a set of input variables to a set of output variables. In seismic reservoir characterisation the input obviously comes from seismic data. This can be in the form of amplitudes, or single and/or multi-trace attributes derived from one or more seismic volumes (e.g. full stack, near stack, far stack, intercept, gradient, inverted acoustic impedance etc). Input may also come from other sources (e.g. co-ordinates, two-way time, geological features etc). Basically any variable that is available at each prediction position and which may be related to the desired output can be used. The output depends on the type and design of the network and how the trained network is applied. The results are two-dimensional (grids) if the network is steered along an interpreted horizon. Three-dimensional results (volumes) are obtained if the network is applied on a trace-by-trace and sample-by-sample basis. Pseudo-well simulation In many fields, there is only limited well control and thus there may be a problem that data is not truly representative of the variations in the data. Hence, the inversion is ill-based. This problem can be bypassed by simulating additional pseudo-wells with associated synthetic seismograms (de Groot, 1996). These are stratigraphic columns with attached well logs but without spatial locations. The method assumes geologically and petrophysically correct simulations and good synthetic-to-seismic matches. These pseudo-wells are representative for the area and can be seen as possible geologic realizations, i.e. each can be the next newly drilled well. For this study, three hundred pseudo-wells with sonic, density (hence impedance) and porosity logs were simulated. The variations in stratigraphy and log response were derived from real well data. The simulator is based on a constrained Monte Carlo procedure which is steered by geological knowledge (de Groot, 1995). Geologic knowledge was incorporated in the simulation model to cover the ranges, which are to be expected in the study area. Sonic and density distributions are correlated with a 0.9 cross-correlation coefficient. Gas columns are not simulated in this case, because the reservoirs occur at a depth of approx. 4km where the effect of gas is not detectable on seismic. For each stratigraphic unit, rules and constraints were implemented. For example, 40% Net-to-Gross in the middle reservoir layer, always a shale to overly the reservoir, and volcanic intrusions occurring only in 50% of the wells. For each pseudo well a synthetic trace is generated, using the convolution model. Segmentation of seismic character In the unsupervised (or competitive learning) approach the aim is to find structure in the data themselves and thus to extract relevant properties
/ features. Seismic waveforms around an interpreted horizon are segmented (clustered) into a specified number of segments. Each segment is characterized by its waveform shaped class center. Mainly visual inspection of these class centers is used to determine the optimal number of classes for segmentation of the waveforms, for this study 8. The Unsupervised Vector Quantiser (UVQ) network first has to learn how to segment the seismic waveforms. This training is done on a representative selection of seismic waveforms, e.g. every 10th Inline and Crossline a waveform is extracted. The network learns to cluster the input into a pre-defined number of segments. We can do this kind of segmentation with any seismic attribute. The advantage of doing it with the seismic amplitudes within a certain time window is that the center vectors resemble seismic waveforms which facilitates the interpretation. Moreover, the segmentation is based on the entire seismic waveform rather than some derived attributes. Application of the network to the entire volume(s) yields two outputs at every sample position: the segmentation result i.e. the index of the winning segment and the match i.e. a measure of confidence in the segmentation. This is a nonquantitative result showing only areas with similar seismic characteristics. In the interpretation of these patterns one must take into account that the seismic response pertaining to a certain geological sequence is smeared across overlying and underlying sequences. Vice-versa, the response from these units may pollute the level of interest. Moreover, if the extraction window is not parallel to the stratigraphy as in our case, we are cutting through the geology and the results become difficult to interpret. With these limitations in mind we can still extract valuable geological and petrophysical information from the observed patterns. The interpretation can be based purely on geological insight but a more quantitative analysis can be done using the well data. Simulated and / or real wells are segmented by the trained UVQ network and the resulting well groups are analyzed for geological and petrophysical content. Quantification of segments To quantify the different seismic classes, the 300 pseudo-wells are segmented by the network according to the corresponding synthetic seismic response. In other words each synthetic seismic response is compared to the UVQ class centers and is assigned to the class it resembles most. The segmentation result is used to analyze geological and petrophysical variations per segment. In this case, 300 simulated wells were segmented into the 8 segments. Subsequently, relevant well features (e.g. porosity and N*Phi) are extracted from the well group in each segment. Analyzing these features reveals where the segments differ in terms of geological and petrophysical content. Table 1 shows the difference in porosity and N*Phi for the 8 segments. Except for class 2, the pseudo-wells are quite evenly distributed over all segments indicating that the pseudo-wells cover the seismic variety of our study area. No wells were classified as class 2, which is therefore missing from the table. Fig 1 Neural network topology for porosity prediction Usually one class acts as a garbage bin to collect all noise traces. None of the pseudo-wells has similar low amplitude synthetics as in class 2, which makes it most probably noise and not related to a reservoir feature. Class 1 and 8 can be quantified as good reservoir, i.e. high porosity and NTG. On the other hand, class 3 and 6, are of lower quality, i.e. low porosity and NTG.
1 3 4 5 6 7 8 Phi(%) 13 11 12 12 12 14 15 N*Phi 3.7 2.5 3.2 3.3 2.6 3.3 4.3 well with the known stratigraphy of the Rotliegend in the area. Table 1 Quantification of 8 UVQ segments Volume transformation to porosity The supervised approach requires the presence of a representative dataset comprising seismic signals with corresponding geological / petrophysical information. Neural networks (MLP) are then trained to quantify the seismic response into desired geological and/or petrophysical target quantities. The neural network input variables were taken from the synthetics and the acoustic impedance traces of the pseudo-wells. Seismic waveforms of [-20,20] ms. length were extracted relative to a reference time, sliding with 4 ms. steps. Hence, seismic waveforms of 40 ms. length were taken at -10, -6, -2 ms. etc. In the same way the amplitude of the synthetic impedance trace was extracted and given to the network. Also the reference time itself served as an additional input node to the neural network. Fig. 1 shows the neural network topology. The porosity and impedance logs for this purpose were converted to time using the sonic log and resampled to 4 ms using an anti-alias filter. To avoid overfitting the 6 real wells were used as test data during the training of the network. Overfitting is a process, which may occur with prolonged training when the network starts to recognize individual examples from the training set and deviates from the general trend. Overfitting is especially a problem when the training sets are small (few wells) and the networks are large (many nodes in the hidden layer means more degrees of freedom, hence more complicated functions can be modeled). It is good practice to use a number of examples as blind test locations. In this study the 6 real wells were used to validate the inversion results. Fig. 2 shows the porosity predictions versus the original porosity trace at one blind test locations. All 6 blind test predictions are very good, hence increasing our confidence in the neural network performance and the representativeness of the pseudo-wells. Fig. 3 shows the porosity prediction on one inline out of the 3D porosity volume. The prediction agrees Conclusions Fig. 2 Porosity comparison The following conclusions are drawn: Quantification of the UVQ segments indicates that segment 1 and 8 can be characterized as good quality reservoir, 3 and 6 as lower quality reservoir. The most interesting result is obtained with the volume-based neural network prediction technique. The predicted porosity traces fit almost perfect to the original porosity trace for the blind test wells. The pseudo-wells, generated within the GDI software, have proven their value in the MLP predictions and UVQ analysis quantification.
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