JOB Pertamina-Medco Energy Tomori Sulawesi, Indonesia 2) Pertamina Hulu Energi, Indonesia 3) Rock Fluid Imaging Lab., Indonesia ABSTRACT

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1 Fracture and Carbonate Reservoir Characterization using Sequential Hybrid Seismic Rock Physics, Statistic and Artificial Neural Network: Case Study of North Tiaka Field Deddy Hasanusi 1, Rahmat Wijaya 1, Indra Shahab 2 Bagus Endar B. Nurhandoko 3 1) JOB Pertamina-Medco Energy Tomori Sulawesi, Indonesia 2) Pertamina Hulu Energi, Indonesia 3) Rock Fluid Imaging Lab., Indonesia ABSTRACT Tiaka field is located in the Senoro-Toili block at the eastern arm of Sulawesi, Indonesia. The main hydrocarbon bearing reservoir is a lower Miocene carbonate sequences which posses a dual porosity system both matrix and fracture. Actually, the carbonate rock characterization is quite complex, because of their matrix, pore system, and also consider of chemical reaction produced from fluid interaction in interior wall of their pores space. and also their wave propagation system through in carbonate reservoir. This carbonate complexity is required special treatment to precisely characterize the reservoir. In this paper, the very latest technology for carbonate complex reservoir characterization using hybrid seismic rock physics, statistic and artificial neural network will be presented. This methodology enable in integrating a huge size of various data set to produce coherence correlation among input data and their target. The data set consist of core (i.e: lithology, lithofacies, fracture intensity, fracture width, porosity), well log (i.e. gamma ray, density, Sw, porositas, resistivitas etc.), multi-attribute either pre-stack or post-stack of a different vintages of 2 D seismic lines and seismic rock physics. The whole of input data was trained together using natural workflow which is also combined with statistic and artificial neural network. Afterwards it is used to predict several reservoir parameters. This method is applied on North Tiaka Field to predict the lateral lithofacies, fracture, porosity, and their fluid or hydrocarbon distribution. In addition, the whole process of reservoir parameter prediction is done by using natural algorithm based on lithofacies prediction, therefore the lithofacies is the first task which should be done before characterizing the other properties of reservoir. By using these approach, its can produce high accuracy on the reservoir parameter prediction. The accuracy of testing process show that predicted parameter reservoir on the average 90 percent match with reservoir parameter in the existing wells. 1. BACKGROUND Carbonate rock has naturally high complexity. The form and nature heterogenity of carbonate rocks often look similar to the response on seismic data and well logs. Even the complexity of pore shape, matrix, the matrix intensity, lithology and facies of carbonate rocks play an important role which generate an ambiguity toward the development and exploration of the hydrocarbon potential. Although these parameters are the key parameters in the success of the oil field development, the better understanding of reservoir parameters and spatial distribution are very important to enhance the reservoir character prediction similar with actual field conditions. Prediction of porosity, permeability, and type of fluid saturation are the key variables needed to characterize a reservoir for the lateral distribution of the hydrocarbon zone. The high porosity zone and even the volume of hydrocarbons and their direction of fluid flow enable to optimize production from the field. The properties of each variable are unique for each rock, but the response of the wave parameter and well data are sometimes overlapping to a different facies, so that the process of prediction needs to be done by combining seismic rock physics and well log data with the rules of statistics and artificial neural networks. The method used to perform the reservoir characterization has an important role to produce a comprehensive conclusion which includes all the information ranging from core, well logs even seismic data. Meanwhile, the conventional interpretation technique will be produced partial and not as comprehensive and integrative result, it is because the processes of interpretation are mutually independent. Then special techniques are used to characterize these carbonate reservoir using a combination of core data, seismic rock physics, well log, seismic data, statistic, and artificial neural network on a integrated system with the base of each reservoir property prediction on the facies prediction results (Nurhandoko, 2008). Thus, the identification of their facies type becomes very important to know beforehand as a basis to determine the other reservoirs properties. Furthermore, other reservoir properties such as porosity, permeability, and fluid saturation types are predicted. Many other researchers are also trying to explore methods in facies predictions are Mukerji, et.al

2 (2001), Murroquin et.al (2009a), Murroquin et.al (2009b), Michelena et.al (2009), and Winchen et.al (2009). But, their technique method different to that done in this paper. Because, the method used in this paper has a natural sequential workflow which using combination of core, well log, seismik, and then artificial neural network is combined with the statistical technique. This technique can produce a skimming facies with high-resolution and still keep their pattern produced by the clustering system (self organizing map). So that can reduce risks associated with drilling wells, reducing the exploitation costs, increase reserve discovered, and increase production with additional drilling location on the site. 2. METHODOLOGY Reservoir properties characterization process of TIAKA Field is selected by using the collaboration of all existing data with seismic rock physics. Seismic rock physics was chosen because it has the ability to connect the parameters of seismic waves and reservoir parameters. After the collection of seismic rock physics data through laboratory measurements, reservoir parameter prediction process can actually be done directly, but the problem is rock physics data usually complex and non-linear. Therefore, in this paper combination both strength between seismic rock physics, statistic (PCA and Bayesian) and artificial neural network is used together to solve this matter. Figure-1 Artificial Neural Network Workflow Illustration Here we use one type of artificial intelligence which called as artificial neural network. A neural network is a system that can be described as a massively parallel-distributed processor made up of simple processing units called neurons, which has a natural tendency for storing experiential knowledge and making it available for use. It resembles the human brain in the following respects: (i) knowledge is acquired by the network from its environment through a learning process. (ii) Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge. It is also known that the combination of many simple neurons into one system is the resource of increased computational power. Figure-2 Diagram flow of reservoir characterization Here we use one type of artificial intelligence which called as artificial neural network. A neural network is a system that can be described as a massively parallel-distributed processor made up of simple processing units called neurons, which has a natural tendency for storing experiential knowledge and making it available for use. It resembles the human brain in the following respects: (i) knowledge is acquired by the network from its environment through a learning process. (ii) Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge. It is also known that the combination of many simple neurons into one system is the resource of increased computational power. Generally, artificial neural network algorithm is chosen to support each prediction process. The utilization of artificial intelligence is critical because of its capabilities to handle complex problems, huge data (many attributes, many correlations) and non linearity. In addition, neural networks and statistics can also connect some information from the core that has a very thin scale in centimeters, the well log data in feet scale and seismic waves that has meters scale. Stages of the required process of reservoir characterization property as set forth in the flow chart illustrated in Figure-2 are as follows: Stage-1 lithofacies Prediction, in this stage several type of inputs required such as: lithofacies from core information, well log data (GR, Sonic, Neutron Porosity, Densitas and Resistivitas), seismic data including their attributes (Instantaneous Amplitude, Instantaneous Frequency, Integrated Absolute Amplitude, and acoustic impedance). The used Methods is hybrid of supervised and unsupervised Neural Network. In the first time well log is combined together with core data using artificial neural network to produce knowledge, which will be used for facies prediction

3 along well. Self Organizing Maps is used for classifying data based on seismic wave s trend and their attribute (Instantaneous frequency, integrated absolute amplitude and instantaneous amplitude and the other type of attribute must be generated previously), followed by Backpropagation-1 for data links seismic with facies in wells, then proceed with the Backpropagation-2 to improve the resolution of facies prediction results in whole section or seismic volume. Stage-2 fracture distribution prediction. Some of the strategies are used to make the results prediction of these fractures more accurate. First of all fracture intensity is defined through the observation of the core data information. Then they are connected with the well log and lithofacies information (resulted from stage-1) to predict the distribution of fractures in whole areas in well that do not have information about fractures. Multiattributes that have a relationship with fracture information, such as acoustic impedance and coherence represented their similarity of uniformity are also generated by deriving from seismic data. Then, these attribute is tied to fracture in well using learning artificial neural network to get knowledge which usefull for fracture prediction in whole section or volume. Then, curvature attribute is also generated from structure map which show that where the basin will have a crack probability greater than sloping areas. In addition, fault map is connected with rock mechanic measurement to describe scope of fractures distribution formed induced by fault influenced. Furthermore, predicted fracture section is combined with curvature, fault induced, and the other attribute using seismic rock physics and artificial neural network to produce final fracture prediction on TIAKA field based on lithofacies distribution. Stage-3 Porosity Distribution Prediction. In this case, because of TIAKA field is influenced by the presence of fractures, then the reviewed here also a double porosity system of fracture and matrix porosity. The type of input data required here is fracture porosity derived from fracture intensity and fracture aperture core observation or resulted from image core processing, petrophysic, well log data, seismik dan lithofacies. On this stage the process analogically similar with lithofacies prediction. But the methods which is used are consist of statistical bayesian, back propagation Neural network, and base of the process is lithofacies existence distribution. Stage-4 Fluid Type Prediction. After obtaining the distribution of lithofacies, fractures, and their porosity, the next step is to predict the distribution of the fluid type that fills the pores such as gas, oil or brine. Input is needed here: rock physics data, well test data, well logs, seismic and their attributes (post-stack, acoustic impedance, shear impedance, lambda-rho, mu-rho, and also attribute which is generated in the previous stage). Then the data is trained with known reservoir parameters such as porosity, lithology and fluid type. The method which is used here are statistical methods PCA (Principal Component Analysis), Bayesian and neural networks. Figure-4 Fracture and reservoir properties distribution map on TOMORI formation of TIAKA Field. a) Fracture Prediction Map, b) Fracture Porosity Prediction Map, c) Matrix Porosity Prediction Map, d) Fluid Distribution Prediction Map. PREDICTION RESULT Figure-3 shows that on the Top of Tomori, a total of 12 classes of facies surfaced up in the model. The facies map resulting from the predictive modeling shows an overall predomination of fractured recrystalized wackestone facies (sky blue colour). This is in agreement with the majority of facies described from the cores. The other 11 classes of facies are: shales, sandstones, calcareous shale, fractured dolostone, fractured dolomitized grainstone, fractured dolomitized wackestone, fractured dissolved wackestone, fractured dissolved boundstone, fractured recrystalized grainstone, fractured recrystalized boundstone, non-fractured recrystalized wackestone. In fact the last class of facies (non-fractured recrystalized wackestone) is the only non-fractured class occurs in the model. And it is only develop as a minority, small, tiny cell in the NNW of Tiaka-3. The other classes of facies are fractured. This is confirming that we are dealing mostly with fractured carbonate reservoir in Tomori Tiaka Field. Figure-3 Lithofacies prediction Map The sky blue facies: fractured recrystalized wackestone served as a background in the overall

4 facies distribution model of the TOMORI limestone. We can almost say that it was distributed everywhere in Tiaka structure, except in the onethird northeastern part of the block boundary, where we started to have calcareous shales occupying most of the area there. Learning from the occurrences of the shale (non calcareous), which also coincide with the calcareous shale (gray color) distribution in a way that they occur in the periphery along the calcareous shale break which separated the northern FRW (fractured recrystalized wackestone) with the southern one, it seems like there are two distinctive facies compartments of the Tomori limestone in this level. They are separated by an area interpreted as mixclastic-carbonate depositional settings; where we had shales, calcareous shales, sands (yellow colour) and even dolostone (orange) and dolomitized facies (red and vermillion colours). The dolomitization processes associated with this break might be explained with the proximity of the original carbonate body to the exposure of meteoric water which enabled them to exchange their ions (Mg-Ca) so as to leading to dolomitization. The resulting yellow, orange, vermillion, and red colour facies are interpreted as the best reservoir facies due to their dolomitization characters. Based on this facies map, Tiaka-3 and Tiaka-2 n top Tomori level can be interpreted as having different compartment character compared to the rest of the other Tiaka wells. They are located on different facies compartments separated by mix-clasticcarbonates facies with the other well location. Implication of this, all the reservoir modeling simulation scheme should consider different fluid system different reservoir in Tiaka 2 and 3 compared to other Tiaka wells. Figure-4a show fracture probability medium to high on TOMORI limestone formation. The high intensity fractured areas on the Top of Tomori Limestone are located mainly in the following 4 areas: To the west of Tiaka-3 well following the trace of the NNE-SSW fault with approximately km length of overall trend toward the SSE of Tiaka-3. Some east-west minor crossing trends are also apparent (and in fact become the area with highest fracture intensity within the trace) these are probably fractures associated with transfer zones between two segments of linear faults. No well has been drilled in this trend, possibly due to the very low position of its structure; lower than Tiaka-3, which is the lowest structure which has been drilled so far in the area. (a) (b) (c) (d) Figure 4. Fracture and Reservoir properties prediction of TIAKA Field. a) Fracture Prediction, b) fracture Porosity, c) Matrix Porosity Prediction, and d) Fluid Type Prediction.

5 To the east of Tiaka-3 well, which is also following a fault trend with accurate trace of a total of km length; but the fault extension is more toward the northern part of Tiaka-3 compared to the first anomaly trend. No well has been drilled in this trend, although possibly in some area the structural position is higher than Tiaka-3 (and hence, have a possibility of containing hydrocarbon) Accurately crossing the Tiaka-2 well, this is in fact following the trend of the fault connecting Tiaka-2, Tiaka-10 and Tiaka-5. Some minor high fracture intensity cross-trends of east-west and NW-SE directions are also observed probably indicating fractures associated with transverse faults between segments of the main fault trend. Right adjacent to the west of major-main thrust faults covering the area of Tiaka-9, 4, 8, 5, and 1, the area to the west of Tiaka- 7, and the area to the NNE of it. These are all proven hydrocarbon bearing and producing area. Major compartmentalization between Tiaka- 3/Tiaka-2 and the rest of Tiaka wells as observed in the facies map, cannot be seen in this fracture map. This may suggest that the fractures are not facies selective, e.g.: there is no direct relationship between facies and fracture formation. Fractures are mostly developed due to tectonics, which is in association to the major thrust fault events of the Tiaka structure. Figure-4b and Figure-4c it shows that fracture porosity is about to (Figure-4b). It is very small compared with matrix porosity which has range about Actually, matrix porosity just describes pore size. This can be very large in size, but the one pore with the other pore not necessarily connected. Conversely, fracture can be very small in size but this character like a pipe. Hence, although fractures porosity is very small compared with the effective porosity, but it will contribute a greater flow of much larger than matrix porosity. Finally, Fluid distribution map predicted as shown in Figure-4d shows that the fluid distribution resulted from the modeling exhibits a very pronounced compartment of northern and southern reservoir systems with a distinct boundary to the north of Tiaka-10 area. The lowest oil level in the southern compartment is around 1925 meter subsea depth, while in the northern area it is in the range between meter subsea depths. Tiaka-3 and Tiaka-7 are all depicted as having none or minor oil fluid content. These are probably due to the less fracture developed in the area and also due to the low structural position of the wells. Other implication of the two compartments observed in the fluid model is that there were probably two distinct episodes of hydrocarbon charging to Tiaka structure; or during the charging they were both charged by different migration route system (not being connected one to another). The charging was most likely facilitated thru the major thrust fault to the east of the Tiaka structure. CONCLUSION The Technology had been used for characterizing North Tiaka field the combination of seismic rock physics, statistics, and artificial neural network. The application of this method at north TIAKA field is used to predict lithofacies, fracture, porosity, and fluid distribution. Prediction results turn out to be accurately where average correlation value between predicted parameter and reservoir parameter around 90 %. Reservoir characterization task on TIAKA field produce several result as listed below: 1. Reservoir of TIAKA field is a heterogeneous carbonate platform 2. Lithofacies distribution appears dominated by wackestone recrystallin fracture type. 3. Porosity Distribution is indentified, and consists of both fracture and matrix porosity 4. fluid distribution map is accurately predicted and enable to distinguish between the distribution of oil and salt water or brine. REFERENCE Nuhandoko, B.E.B., Susilowati, Mubarok, S., Hariman,Y., Kurniadi, R., Widowati, S., Ari, A., 2008, Teknik prediksi jenis facies dan diagenetik kombinasi metoda jaringan syaraf tiruan unsupervised dan supervised, Dokumen patent Rock Fluid Imaging Lab., Dirjen HAKI Mukerji, T., Jørstad, A., Avseth, P., Mavko, G., and Granli, A.J., T. Mukerji, A. Jørstad, P. Avseth, G. Mavko, and J. R. Granli, 2001,Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics, Geophysics, V. 66, No. 4, P Marroquín, I.D., Brault, J.J., and Hart, B., 2009, A visual data-mining methodology for seismic facies analysis: Part 2 Application to 3D seismic data, Geophysics, V. 74, no. 1, P13-P23. 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