Anne Randi Syversveen

Fault facies modeling: Technique and approach for 3-D conditioning and modeling of faulted grids

Faults in nature commonly affect surrounding rock volumes and can as such be described as fault envelopes with a given internal geometry and architecture. Modeling techniques currently employed when modeling faults in petroleum reservoirs are mostly two-dimensional (2-D); hence, a need is present for more accurate and realistic description and quantification of deformational architectures and properties to accurately predict fluid flow in fault zones. Fault facies (FF) modeling is a concept for three-dimensional (3-D) fault zone characterization, facies modeling of fault rocks and fluid flow simulation, which is presented here and demonstrated by the use of a synthetic fault model. FF modeling is performed by first generating a 3-D grid of the fault envelope, which includes the conventional fault plane. Second, a kinematic strain calculation is executed in the FF grid. The strain parameter is used to calculate a fault product distribution factor (FPDF), which describes the fault displacement in the fault envelope. This parameter together with strain distribution is subsequently used to condition the fault model for facies modeling. Finally, FF modeling is executed. To achieve adequate flexibility and realism, pixel-based modeling is combined with object-based modeling methods to populate the FF grid with facies. This synthetic model shows that it is possible to honor structural outcrop observations in fault zones, and FF modeling is able to produce realistic looking fault zone deformation structures in 3-D. It is possible to implement faults with varying width and displacement, although the FF grid itself has a regular fixed width. This is highly advantageous as compared to controlling the fault geometry with the grid itself. We propose that FF modeling can improve fault zone characterization and also capture fluid flow uncertainty in fault zones in a more realistic way than is possible with 2-D methods.

Fault displacement modelling using 3D vector fields

In history matching and sensitivity analysis, flexibility in the structural modelling is of great importance. The ability to easily generate multiple realizations of the model will have impact both on the updating workflow in history matching and uncertainty studies based on Monte Carlo simulations. The main contribution to fault modelling by the work presented in this paper is a new algorithm for calculating a 3D displacement field applicable to a wide range of faults due to a flexible representation. This gives the possibility to apply this field to change the displacement and thereby moving horizons and fault lines. The fault is modelled by a parametric format where the fault has a reference plane defined by a centre point, dip and strike angles. The fault itself is represented as a surface defined by a function z = f(x,y), where x, y and z are coordinates local to the reference plane, with the z-axis being normal to the plane. The displacement associated with the fault outside the fault surface is described by a 3D vector field. The displacement on the fault surface can be found by identifying the intersection lines between horizons and the fault surface (fault lines), and using kriging techniques to fill in values at all points on the surface. Away from the fault surface the displacement field is defined by a monotonic decreasing function which ensures zero displacement at a specified distance from the fault. An algorithm is developed where the displacement can be increased or decreased according to user-defined parameters. This means that the whole displacement field is changed and points on horizons around the fault can be moved accordingly by applying the modified displacement field on them. The interaction between several faults influencing the same points is taken care of by truncation rules and the ordering of the faults. The method is demonstrated on a realistic synthetic case based on a real reservoir.

A hierarchical modelling approach to combining environmental data at different scales

Long-transported air pollution in Europe is monitored by a combination of a highly complex mathematical model and a limited number of measurement stations. The model predicts deposition on a 150 km × 150 km square grid covering the whole of the continent. These predictions can be regarded as spatial averages, with some spatially correlated model error. The measurement stations give a limited number of point estimates, regarded as error free. We combine these two sources of data by assuming that both are observations of an underlying true process. This true deposition is made up of a smooth deterministic trend, due to gradual changes in emissions over space and time, and two stochastic components. One is non- stationary and correlated over long distances; the other describes variation within a grid square. Our approach is through hierarchical modelling with predictions and measurements being independent conditioned on the underlying non-stationary true deposition. We assume Gaussian processes and calculate maximum likelihood estimates through numerical optimization. We find that the variation within a grid square is by far the largest component of the variation in the true deposition. We assume that the mathematical model produces estimates of the mean over an area that is approximately equal to a grid square, and we find that it has an error that is similar to the long-range stochastic component of the true deposition, in addition to a large bias. Copyright 2003 Royal Statistical Society.

Facies Modelling in Fault Zones

Traditionally fault impact on fluid flow is included by assigning transmissibility multipliers to flow simulation grid cell faces co-located with the fault plane (Manzocchi et al. 1999). A new method, called Fault Facies modelling (Tveranger et al. 2004, 2005), captures fault impact by considering faults as deformed rock volumes rather than simple planes. Architectures and petrophysical properties of these deformed volumes (i.e. fault zones) are linked to a range of factors such as lithology, host rock petrophysical properties, tectonic regime, orientation, magnitude, and distribution of stress, as well as the burial depth at the time of faulting. By understanding these links and identifying bounding values for distributions and parameters, fault zone architectures and properties, as well as uncertainties attached to these, can be forecasted. The fault facies approach allows 3D features such as anisotropic permeability fields, capillarity effects and tortuosity of flow paths inside the fault zone to be explicitly represented in the reservoir models. Furthermore, on the simulation grid scale, flow between cells on opposite sides of faults, as well as any uncertainty attached to this, can be estimated a priori rather than set deterministically a posteriori using history matching. The paper compares fluid flow behaviour of conventional transmissibility multiplier-type fault property models and fault facies type models through a series of simple tests. The study demonstrates that the fault facies concept is a technically feasible methodology that represents an alternative or supplement to standard industrial fault modelling methods.

Modeling Facies Bodies and Petrophysical Trends in Turbidite Reservoirs

A two-stage approach for modeling turbidite reservoirs is presented. The first stage is to model the distribution and geometry of turbidite sandbodies using an object model that can be constrained to local vector directions. The facies objects are individual turbidite sandbodies that are locally aligned to a vector parameter to model the effect of local seafloor topography on sandbody geometry and orientation. The second stage involves modeling petrophysical variation within each turbidite body. This is done by using a variety of intrabody trends, which describe vertical and lateral variation in the petrophysical properties. These variations are the result of grain-size and sorting trends within each turbidite sandbody.

A Study on How Top-Surface Morphology Influences the Storage Capacity of CO2 in Saline Aquifers

The primary trapping mechanism in CO2 storage is structural trapping, which means accumulation of a CO2 column under a deformation in the caprock. We present a study on how different top-seal morphologies will influence the CO2 storage capacity and migration patterns. Alternative top-surface morphologies are created stochastically by combining different stratigraphic scenarios with different structural scenarios. Stratigraphic surfaces are generated by Gaussian random fields, while faults are generated by marked point processes. The storage capacity is calculated by a simple and fast spill-point analysis, and by a more extensive method including fluid flow simulation in which parameters such as pressure and injection rate are taken into account. Results from the two approaches are compared. Moreover, by generating multiple realizations, we quantify how uncertainty in the top-surface morphology impacts the primary storage capacity. The study shows that the morphology of the top seal is of great importance both for the primary storage capacity and for migration patterns.

Comparing Facies Realizations – Defining Metrices on Realization Space

A typical workflow for generating flow simulation grids goes through a facies modelling step. This typically involves setting up a stochastic model that is supposed to capture the important properties of the facies bodies in the reservoir volume in question and their uncertainties. This may be difficult or impossible within a particular modelling framework. Either we end up with a model too simple to be able to reproduce the characteristics of the reservoir, or the model parametres become too many and too difficult to specify. Hence users ask for methods able to reproduce the properties of a training image automatically. In any case one would like objective measures of similarity of facies realizations so that one is able to determine if a set of realizations have the properties that one wants. Here we discuss possible components in a metric on a space of facies realizations and present an implementation of a facies realization analyzer program. The algorithm simply scans a number of realizations and computes the global volume fractions of each facies and the number of facies bodies of each type. Then it computes the surface areas, volumes, and extensions in each directions for the bodies and performs simple statistical analysis of the realizations and compare it with properties of a training image. We present results of applying this software on facies realizations produced with variogram based methods, multipoint methods, and sequential Markov random fields. The analyzer algorithm is fast, applicable in 2D and 3D, and the results are in excellent agreement with the subjective impression of similarity or dissimilarity obtained through visual inspection.

From Geological Knowledge to Good Decisions Using Simple Stochastic Models: A North Sea Case Study

The decision process for one of the partners in the development of the Alvheim field is presented. The challenge was to turn relatively small Palaeocene structures consisting of both unproven prospects and proven oil and gas reserves into a field development with a good economic performance. The paper describes the stochastic model that was buildt to support decisions in the early phases of the field development. To pinpoint the essential elements for a model is difficult task. Not only the characteristics of the field, but also the decisions that the model is supposed to support, must be considered. A simple stochastic model was buildt. An exploration type of approach was used. Essential elements are volume-depth curves and modeling of seismic uncertainty and uncertainty in fluid contacts. Using the model, it was possible to explore important upside potensial seen in geophysical evaluations and answer if and in what sequence further wells should be drilled. An improved understanding of the value for different drilling strategies was gained by studying distributions for in-place oil and gas volumes for various scenarios.

Impact of Top-reservoir Morphology on CO2 Sequestration

Models used for evaluating CO2 plume behaviour in the subsurface often employ simplified geological reservoir descriptions. Experiences from the petroleum industry show, however, that geological heterogeneities significantly influence fluid flow. The present study addresses the need for evaluating the impact of realistic geology on CO2 behaviour in the subsurface. We here demonstrate the effect of adding realistic complexity to the top reservoir morphology. A sensitivity matrix consisting of combinations of depositional and structural irregularities creating relief along the top of a reservoir was set up and the resulting models run in a fluid flow simulator monitoring CO2 plume dynamics. Results demonstrate the interaction between specific geological features and resulting plume behaviour and added retention capacity. Our study highlights the need to include realistic geology in models forecasting migration behaviour in subsurface reservoir.

Local Update of Object-Based Geomodels

A method for local updating of object-based facies models by further development of already existing software is presented. An existing facies realization is adjusted to match new well observations by changing objects locally or adding/removing objects if required. Parts of the realization which is not influenced by the new wells are not changed. Local update of a specified region of the reservoir can be performed, leaving the rest of the reservoir unchanged or with minimum change due to new wells. The main focus in the method is on the algorithm implemented to fulfill well conditioning. The effect of this algorithm on different object models is presented through several case studies. These studies show how the local update consistently includes new information while leaving the rest of the realization unperturbed and thereby preserves the good history match. Introduction Rapid updating of static and dynamic reservoir models is important for reservoir management. Continual maintenance of history matched models allows for right time decisions to optimize the reservoir performance. The process from drilling a new well through to update of the static model and history matching of the new model is often a time consuming process. Static reservoir models and history matches are only updated intermittently and there is typically a one to two year delay between the drilling of a new well and the generation of a reliable history matched model which incorporates the new information. This paper presents new algorithms which allow rapid updating of static reservoir models when new wells are drilled. The static model update is designed to keep as much of the existing history match by locally adjusting the existing static model to the new well data. For many reservoir types, stochastic object models provide the best reservoir description for understanding reservoir connectivity and for history matching purposes. The algorithms presented in this paper are designed for local updating of such models. As the name implies, object models use a set of facies objects to generate a facies realization. The model gives control of the geometry of these objects and the interaction between them, so geological input is important. In addition to the geometrical control, the main advantage of object models is the ability to create petrophysical trends inside objects and to correlate facies objects between observations in different wells. Two main groups of objects are treated by the algorithm for local update. The first is fluvial channels. These typically run through the reservoir along a main direction line. Holden et al. (1998) describes how the modeling and simulation of such objects is performed. The other group consists of general objects characterized as axial or backbone. These are generally of limited size. Hauge et al. (2006) and Lia et al. (1996) give a description of these models. In the next section the object models are briefly presented. An algorithm is developed to make the updated model condition all well observations correctly. Where there is conflict between well observations and an object, a local change in the object is attempted. For complex well patterns, such changes may not be sufficient to solve the problem; in these cases, the conflicting object is removed. Similarly, for observations that