Reservoir Monitoring. special topic

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1 From seismic interpretation to reservoir model: an integrated study accounting for the structural complexity of the Vienna Basin using an unstructured reservoir grid Caroline Milliotte 1* and Stephan K. Matthäi 2 show how a former structurally complex oil reservoir that is evaluated as a geological CO 2 storage complex, can be modelled at a very high level of geological detail using a new workflow involving unstructured finite element meshes. The resulting simulation model includes discrete representations of complex intersecting faults. T o mitigate climate change induced by industrial CO2 emissions, new techniques such as geological Carbon Capture and Storage (CCS) are being investigated. The successful implementation of CCS is challenging, involving detailed characterization, modelling and simulation of the performance of a candidate storage complex, to become aware of potentially adverse emergent behaviour arising from CO 2 injection for instance: non-radial, potentially highly localized CO 2 spreading, exhaustion of the sealing capacity or breach of the confining structures (faults and cap rock), reactivation of sealed faults, and the risk of leakage from the storage site (cf. European Communities Directive on the geological storage of carbon dioxide, 2010). For these reasons, the implementation of CCS requires careful multi-disciplinary assessment, preceding any pilot or full-scale project. The Reservoir Engineering Institute of the Montanuniversitaet Leoben (MUL), Austria, is currently working on the simulation and hindcasting of a CO 2 injection project conducted to enhance oil recovery from a mature reservoir. This research is supported by the Austrian funding agency FFG, and data is provided by an oil and gas company. This company contracted NFR Studies to build an improved reservoir simulation model that captured the structural complexity of the candidate storage site, and conduct property modelling in more detail than achieved previously for field development and production purposes. This paper describes the main steps of the static characterization and modelling of the candidate storage site. This is an oil reservoir depleted in the 1970s, located in the Vienna Basin at about 1500 m deep. It has a lateral extent of 15 km x 7.5 km and consists of a 200-m thick sequence of Mid-Miocene siliciclastics. The deposition environment is pro-grading deltas, and they overlie a conglomeratic layer that is a regional active aquifer. The trap consists of a gentle anticline structure, closed by a complex fault system on its eastern flank. About 80 vertical wells have been drilled through the crest of the anticline and the main stratigraphic markers have been interpreted from logs. Outcrop studies and lidar surveys confirmed that the fault system is active today (Gutdeutsch and Aric, 1988; Hinsch et al., 2005b; Beidinger and Decker, 2010). This made it necessary to evaluate the sealing capacity of the faults, and the structural model had to capture the full vertical extent of the faults, from basement to ground surface. Methodology Starting with the seismic interpretation of the faults in the two-way time (TWT) domain and the construction of a timedomain structural model, a velocity model was built and used for the depth conversion. The depth domain structural model was checked against well markers and consequently some adjustments were made to the fault interpretation. The resulting watertight structural model was used as input for the creation of a fully unstructured reservoir grid, meshed with tetrahedral aligned along faults and horizons. In addition, an innovative petrophysical technique (Ramberger and Skolnakorn, 2001) was used to compute porosity values from the available SP logs. The porosity was simulated in the geological grid, using areal trends derived from a series of regional facies maps per main stratigraphic unit. Porositypermeability correlations were used to populate permeability values in the geological grid. Finally, the properties were transferred from the geologic grid on to the unstructured reservoir grid. Fluid pressure was initialized in the latter, using the CSMP++ reservoir simulator, developed by the MUL. 1 NFR Studies GmbH, Graz, Austria. 2 Institute of Reservoir Engineering, Montan University of Leoben, Leoben, Austria. * Corresponding Author, caroline.milliotte@nfrstudies.com 2014 EAGE 95

2 Figure 1 Inline section (TWT) with initial interpretation of the five main stratigraphic horizons: top of basement (dark green), conglomerates (red), 1 st reservoir (purple), 2 nd reservoir (yellow) and shale cap rock (light green). The 1 st reservoir layer overlies a stratigraphic unconformity and onlaps on the conglomerates (white arrow). Faults can be identified in the north-east (white lines). As the reservoir was developed more than 30 years ago, most of the available data is old; a structural framework model was not available and the existing reservoir model did not contain any faults. Five main stratigraphic horizons and a regional seismic amplitude volume were made available in the two-way time domain (Figure 1). Fault interpretation was inexistent and some stratigraphic markers were missing or duplicated. Seismic interpretation of the major faults The main faults were identified on the available horizon interpretations, especially at the top of the sequence. In the northeast, fault arrays represent the main structure (Figure 2). To interpret the subvertical faults, seismic amplitude was converted into a semblance volume. The signal discontinui- ties induced by the faults were then highlighted, facilitating the picking process of the fault sticks. While interpreting the faults, the corresponding structural model was automatically generated using a volume-based implicit modelling approach and the UVT transform from Mallet et al. (2007). This process enabled a rapid update of the fault interpretation, gaining confidence in the geological realism of the interpreted fault contacts. Velocity model and time-to-depth conversion A time domain geological grid was created for the velocity modelling. From the available 80 wells, only three wells had check-shot data and only one could be used after review of the check-shot quality. For this well, pseudo average velocities were computed, from: i) the known depth of the main stratigraphic well markers and ii) the TWT value, interpolated from the vertical projection of the well markers on to the corresponding two-way time stratigraphic horizons. The pseudo velocity was then calculated, using the following equation: V Depthwell marker, = x21000 x TWT avg pseudo projected wellmarker Figure 2 Top view of shallowest horizon interpretation: complex fault arrays are highlighted (red arrows) in the northeast. Straight features oriented NE-SW are faults with a relatively small throw (perpendicular to the white arrows). The new interpretation includes all of these features. where 2 x 1000 represents the conversion factor from milliseconds TWT into seconds. The pseudo velocity was then transferred on to the time domain geological grid and interpolated. This procedure ensures that the interpolation of pseudo velocity honours the sharp discontinuities induced by the faults and the stratigraphic unconformity. Finally, pseudo velocity was calibrated with the check-shot data and the corresponding correction factor was interpolated in the grid and applied to the entire model. The corrected final velocity was used to perform the time-to-depth conversion of the structural model, meaning that faults, horizons and the geological grid were converted in one single operation EAGE

3 Depth structural model fit to well markers The depth-converted structural model did not match all the well markers. Some adjustments of the fault interpretations were made, getting problematic well markers to be located on the correct side of fault planes. Additionally, some inconsistencies in the original interpretation of the well markers were highlighted by applying the UVT transform procedure: where the first reservoir layer was onlapping on to the conglomerates (0 thickness unit), some of the corresponding well markers indicated a thickness of several tens of metres for this reservoir unit. The inconsistent well markers were assigned to a shallower stratigraphic horizon and the unconformity was preserved in the final depth domain model. The tops of reservoir markers were missing for most of the wells. Using spontaneous potential (SP) logs acting as good indicators for the shale content of clastic intervals, new markers were interpreted and introduced into 19 wells. In order to simulate CO 2 injection and potential viscous or heterogeneity-induced fingering, the lateral extent of the model was increased. The model boundaries are now located several tens of kilometres away from the injection well, also allowing an assessment of the impact of injection on fluid pressure via simulation. Geologic grid Using the integrated volumetric modelling approach of Mallet et al. (2007), implemented in the Paradigm SKUA volume-based modelling system, as soon as a structural model is created, a corresponding geocellular grid can be obtained through contouring of the UVT function. Only the cell size has to be specified. The geological grid conforms with geological time and honours structural and stratigraphic discontinuities, along which cells are being offset according to fault throws, see result section (Figure 6). Property modelling Out of the 80 wells, only 19 have well logs. In particular, spontaneous potential (SP) logs are available. The methodology of Ramberger and Skolnakorn (2001) uses SP and resistivity logs to determine the thickness of sand intervals and their porosity. First, the resolution of the SP logs is enhanced using a forward modelling filter, calibrated with standard SP correction charts; SP values are normalized. Then, an inverse filter is applied to generate a neural network for automatic recognition of correlation patterns. Using the inverted SP log and the resistivity data, the actual sand/shale ratio is estimated. This method targets layered sandstones and is applicable to the prograding deltaic sequence of the Vienna basin reservoir. From the new logs, porosity histograms and variograms are generated one for each stratigraphic unit and reservoir rock type. These statistics serve as input for a sequential Gaussian simulation of porosity on the geological grid. Regional facies deposition maps from Strauss et al. (2006) define areal trends, capturing the orientation and size of the fluvial system deposits (conglomerates) and those of the deltaic systems (reservoir layers). These enter the property modelling in the form of kriging weights. Unstructured reservoir simulation grid The detailed geological grid created on the basis of the UVT Transform (Mallet et al., 2007), only has a representation in parametric space. It cannot be outputted directly to a reservoir simulator. If a corner-point grid representation is used, oblique features must be converted into a series of stair stepping cells to achieve the required regularization. If standard first-order FD stencils are employed in the simulation, the original oblique connectivity is lost in the stair steps (cf., Matthäi et al., 2007). Furthermore, corner-point cell distortions needed to get the grid to conform to curved boundaries and Y-intersections provoke large discretization errors (e.g., King et al., 2006). In the planned assessment of the CO 2 leakage risk, the active faults must be captured accurately. Hence, corner-point discretization is not an option. Considering alternative discretization approaches, it is important to note that, due to the fault and unconformity offsets, the UVT-based geocellular grid is not conforming (i.e., node matching) across such boundaries. However, recently it has become possible to create and output nodematching triangulated fault, horizon, and unconformity surfaces from SKUA. Such boundary representations (BREP) of the structural framework model are also watertight, i.e., the surfaces partition the model volume into unique and fully closed sub-volumes. A node-matching and water-tight BREP permits the creation of fully unstructured volumetric finiteelement meshes, using tools that are standard in other civil engineering disciplines, like in the automotive industry (cf., Matthai et al., 2007, Paluszny et al., 2007). Following the recommendations of Matthäi et al. (2007), a fully unstructured mesh was created, once the water-tight structural depth model had been built for the Vienna basin reservoir. This unstructured grid consists of tetrahedra, triangles, and line elements. The latter represent the well completions embedded into the unstructured mesh. To capture the large-aspect ratio faults and reservoir layers, the grid was adaptively refined. Second only to the ability to discretize arbitrarily shaped geologic objects, this spatially adaptive grid refinement is decisive for the construction of a geologically realistic simulation model as it allows concentrating computational effort and accuracy in the regions of interest. There is an important difference between finite-difference (including corner-point) grids and finite-element meshes: The former is a point-based material property discretization. Since there are no discrete material boundaries, transmissibility multipliers are needed to establish flow 2014 EAGE 97

4 connections crossing implicit boundaries existing between material points. The finite-element method and derived hybrid FEM-node centered finite volume methods used in reservoir simulation (Kim and Deo, 1999, Matthai et al., 2007, Geiger et al., 2009, and Matthai et al., 2012), are based on a piecewise volumetric integration over elements. The elements directly conform with material boundaries. For material property transfer from the geocellular (point-based) grid used in G&G tools in conjunction with BREP, to the unstructured mesh-node matching the BREP, this difference leads to two key requirements: 1) the property mapping must honour the BREP volume decomposition, and 2) point-based material property values obtained by property modelling on fine regular grids in the G&G tools need to be mapped to the spatially adaptively refined meshes, retaining their statistics, spatial correlation structure and integral values (PV, OIP etc.). Property mapping transfer between the geological and the simulation grids Modelled porosity and permeability values are stored at the cell centre points of the geocellular grid. For each stratigraphic layer, these points with property attributes were exported to a corresponding ASCII file block with the following structure: layer name, X, Y, Z, porosity, and permeability. Using the structured-to-unstructured grid property mapping algorithm of Mosser (2013), these values were mapped onto the unstructured mesh for simulation with the CSMP++ reservoir simulator developed at the Montanuniversitaet Leoben, Austria. This mapping honours stratigraphic boundaries, preventing spill-over of property values. Where multiple values fall into a single tetrahedron, area-weighted arithmetic averaging is performed for porosity, while harmonic averaging is applied for permeability. Where the point spacing is smaller than the element size, this algorithm interpolates property values from elements in the neighbourhood using a Laplace equation-based, smooth non-oscillatory interpolation algorithm (cf., Sambridge et al., 1995). To map properties from the point grid to triangulated surfaces when a lower-dimensional fault representation is Figure 3 TWT structural interpretation of the storage complex. Lowest reflector represents the basement; top = ground surface. Note the newly interpreted fault sticks constraining 40 individual faults. Figure 4 TWT water-tight BREP of the storage complex (see text). Fault arrays are captured and clean contacts (X and Y shapes) modelled; vertical NE-SW striking faults are also captured. The unconformity (purple) at the base of the reservoir horizon is preserved. Figure 5 Inline section through the TWT structural model, intersecting the fault arrays. Faults have a vertical extent from basement to ground surface. The anticline structure is honoured, as well as the non-uniform thickness of the first reservoir layer (between purple and red horizons) EAGE

5 Figure 6 Detail of the geological grid (depth domain), showing the cells being cut and displayed at fault intersections as well as at the stratigraphic unconformity (purple layer). used (cf., Juanes et al., 2000), property values are first projected on to the triangular elements, discretizing the faults before the same algorithm is applied in the fault plane. Link with reservoir simulation Once populated with properties, the unstructured reservoir simulation model lends support to multi-physics simulations, using, for instance, the hybrid Finite Element Finite Volume (FEFV) approach implemented using CSMP++ (Matthäi et al., 2007). However, since the properties of the faults are a function of the effective stress, fluid pressure has to be initialized first, taking into account initial saturations, see Milliotte et al. (2013). The permeability of the fault zones is modelled, taking into account available measurements of the in situ stress. Multi-phase flow simulation of the gas-water(oil) system is now possible. Since the Vienna basin field case is an exploited reservoir, history matching with dynamic data can lend further support to the new simulation model before the analysis of the different CO2 injection scenarios is carried out. Results Forty faults were retained in the final structural model (Figures 3-5), preserving the full complexity of their crosscutting relationships: most of the contacts had an X or Y shape. According to Beidinger and Decker (2010), the fault system in the north east of our model corresponds to a negative flower structure, developed in the Miocene and reactivated in the Quaternary. They interpreted the fault arrays as Riedel-type splay faults branching on a main NE-SW fault plane. Their interpretation was made mainly from a series of 2D seismic amplitude crosslines (12) and inlines (3), together with four 2D time slices. Our 3D fault interpretation con EAGE Figure 7 Porosity (dark blue curve), derived from SP (red curve) and resistivity (light blue curve) logs, using method of Ramberger and Skolnakorn (2001). Upper reservoir layer (shale-sand intercalations) in well from the central part of the planned storage complex. firms that the local extension is accommodated by a series of normal faults. Yet, we have not been able to identify any symmetry in the fault arrays. We would rather interpret the structure as a consequence of extension locally induced by the active sinistral Vienna Basin strike-slip fault. While most of the faults were identified as normal faults, one reverse fault was also interpreted. The final fault interpretation fits nicely with Hölzel s (2009) review of the main structural components of the Vienna Basin. The geological grid resolution was chosen as follows: horizontal cell dimension is 50 m; same vertical cell size in the non-reservoir layers and 25 m in the reservoir units. Figure 6 shows a detailed view of the UVT gridding, honouring faults and the stratigraphic unconformity. 99

6 Figure 8 Unstructured reservoir mesh. (Top) fault and boundary surfaces (grey); top of basement (horizontal green surface) and wells (lines). (Bottom) tetrahedra in the basement layer and triangulated surfaces for the stratigraphic layers (green, beige and red horizontal surfaces). The mesh is refined around the fault surfaces, wells and reservoir layers, while a coarse mesh is used in the basement. Figure 9 Fluid pressure initialization of unstructured reservoir simulation model (rainbow colour scheme). Only the reservoir layer is displayed volumetrically. Fluid pressure is also shown for the faults, reaching maximum values at the basement contact. In the reservoir sequence, the porosity log was calculated from the normalized and resolution-enhanced SP logs combined with the resistivity logs. The calculation worked best for the stacked sandstone beds intercalated with shales. Its results are displayed for a well interval between 1000 and 1600 m deep in Figure 7. Porosity ranges around 8% in the shales and around 15-20% in the sands. Local porosity values reach 25%. Permeability has been derived from the newly calculated porosity, using regional correlations. 100 The final reservoir model has an area of 32 km x 13 km and stands 6 km tall, delimited by the basement and the ground surface. It contains 12 million elements. The faults, represented by triangulated surfaces, are part of the mesh. Tetrahedra discreting adjacent layers conform with the layer boundaries and are node-matched along them (Figure 8). The wells have a discrete representation by line elements, again node-matched with volumetric elements that are refined around them EAGE

7 Property modelling was performed in the geologic grid and followed by mapping onto the unstructured mesh. Figure 9 shows a fluid pressure initialization based on a hydrostatic pressure gradient and atmospheric pressure at the model top. Conclusion In order to assess the performance of a potential CO 2 storage complex in an exploited oil reservoir of the Vienna basin, by EU-directive compliant simulation, a detailed structural and geological model was built, using new volume-based modelling tools and an unstructured simulation grid (=mesh). The structural complexity of an array of 40 intersecting, partially active faults was captured including X and Y contact shapes. A stratigraphic unconformity was also captured by the new structural framework model. The innovative and multi-disciplinary workflow presented in this paper involves the creation of a fully unstructured reservoir grid for supporting geomechanical calculations, and an assessment of the sealing capacity of the faults. The next step of this project will be history matching of the new model with historic production data, followed by multi-phase simulations, comparing a range of potential gas injection scenarios. The conforming, adaptively refined reservoir simulation mesh allows multi-physics simulations with hybrid finite element finite volume codes. This will permit tight coupling of the multi-phase flow with geomechanics and reactive transport simulations, achieving the objectives of the performance assessment according to the EU directive. Such simulations are currently being implemented at the Montanuniversitaet Leoben (cf., Mindel and Manasipov, 2013) in the framework of the FFG-funded GEOCCS project. Acknowledgements The authors would like to acknowledge the support of the Austrian FFG research council in the framework of the GEOCCS Bridge 1 project. 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