Fine-scale Geological Model Research Articles

A coarse-scale compositional model

In subsurface flow modeling, compositional simulation is often required to model complex recovery processes, such as gas/CO 2 injection. However, compositional simulation on fine-scale geological models is still computationally expensive and even prohibitive. Most existing upscaling techniques focus on black-oil models. In this paper, we present a general framework to upscale two-phase multicomponent flow in compositional simulation. Unlike previous studies, our approach explicitly considers the upscaling of flow and thermodynamics. In the flow part, we introduce a new set of upscaled flow functions that account for the effects of compressibility. This is often ignored in the upscaling of black-oil models. In the upscaling of thermodynamics, we show that the oil and gas phases within a coarse block are not at chemical equilibrium. This non-equilibrium behavior is modeled by upscaled thermodynamic functions, which measure the difference between component fugacities among the oil and gas phases. We apply the approach to various gas injection problems with different compositional features, permeability heterogeneity, and coarsening ratios. It is shown that the proposed method accurately reproduces the averaged fine-scale solutions, such as component overall compositions, gas saturation, and density solutions in the compositional flow.

A Regional Uplayering Technique for Preserving Reservoir Heterogeneities

An automated sequential statistical algorithm is proposed for nonuniform vertical upscaling (also known as uplayering) that uses the averaged property information (defined as trend) to capture reservoir heterogeneity globally. The fine scale geological model is first divided into multiple reservoir zones based on the geological boundary information and/or the vertical trend information. In each reservoir zone, the horizontal trend information is used to partition the zone into a set of regional blocks, with the intrablock property variance being minimized and the inter-block variance being maximized. Each regional block, regardless of its neighboring blocks, is further divided into a certain number of layers. Thus, this new upscaling method generates nonneighboring connections between the adjacent blocks. This technique can be used on continuous and categorical properties or on any property that is a good discriminator of reservoir flow. The other reservoir properties or any of their combinations can be used as the weighting functions, globally or by zone, to modify the uplayering process, such that the detailed reservoir features, such as the shale barriers, thief zones can be preserved. The proposed algorithm is size independent, scenario independent, and CPU efficient. The advantage of using vertical nonuniform upscaling over uniform upscaling is demonstrated with several case studies for different depositional systems.

History Matching of Production and 4D Seismic Data: Application to the Girassol Field, Offshore Angola

Time-lapse seismic provides a source of valuable information about the evolution in space and time of the distribution of hydrocarbons inside reservoirs. Seismic monitoring improves our understanding of production mechanisms and makes it possible to optimize the recovery of hydrocarbons. Although 4D seismic data are increasingly used by oil companies, they are often qualitative, due to the lack of suitable interpretation techniques.Recent modeling experiments have shown that the integration of 4D seismic data for updating reservoir flow models is feasible. However, methodologies based on sequential interpretation of 4D seismic data, trial and error processes and fluid flow simulation tests require a great effort from integrated teams. The development of assisted history matching techniques is a significant improvement towards a quantitative use of 4D seismic data in reservoir modeling.This paper proposes an innovative methodology based on advanced history matching solutions to constrain 3D stochastic reservoir models to both production history and 4D seismic attributes. In this approach, geostatistical modeling, upscaling, fluid flow simulation, downscaling and petro-elastic modeling are integrated into the same history matching workflow. Simulated production history and 4D seismic attributes are compared to real data using an objective function, which is minimized with a new optimization algorithm based on response surface fitting. The gradual deformation method is used to constrain the facies realization, globally or locally, which populates the geological model at the fine scale. Moreover, a new method is proposed to update facies proportions during the optimization process according to 4D monitoring information.We present here a successful application to the Girassol field. Girassol is a large, complex and faulted turbidite field, located offshore Angola. First, a detailed geostatistical geological model was built to describe reservoir heterogeneity at the fine scale, while respecting 3D base seismic data. Second, the model was constrained to production data and 4D seismic attributes, applying gradual deformation to facies realizations and varying facies proportions. The integration of 4D seismic data led to better production forecasts and improved predictions confirmed by a new seismic survey shot two years after the history matching period. 4D seismic data also contributed to better characterize the spatial distribution of heterogeneities in the field. As a result, the fine scale geological model was improved consistently with respect to the fluid flow simulation model and the observed data. The Girassol study, already presented in (Roggero et al., 2007, 2008), has been updated with recent information and a more detailed presentation concerning the construction of the geological model based on 3D seismic data.

A dual scale approach to production data integration into high resolution geologic models

Inverse problems associated with reservoir characterization are typically under-determined and often have difficulties associated with stability and convergence of the solution. A common approach to address this issue is through the introduction of prior norm constraints, smoothness regularization or reparameterization to reduce the number of estimated parameters. We propose a dual scale approach to production data integration that relies on a combination of coarse-scale and fine-scale inversions while preserving the essential features of the geologic model. To begin with, we sequentially coarsen the fine-scale geological model by grouping layers in such a way that the heterogeneity measure of an appropriately defined ‘static’ property is minimized within the layers and maximized between the layers. Our coarsening algorithm results in a non-uniform coarsening of the geologic model with minimal loss of heterogeneity and the ‘optimal’ number of layers is determined based on a bias-variance trade-off criterion. The coarse-scale model is then updated using production data via a generalized travel time inversion. The coarse-scale inversion proceeds much faster compared to a direct fine-scale inversion because of the significantly reduced parameter space. Furthermore, the iterative minimization is much more effective because at the larger scales there are fewer local minima and those tend to be farther apart. At the end of the coarse-scale inversion, a fine-scale inversion may be carried out, if needed. This constitutes the outer iteration in the overall algorithm. The fine-scale inversion is carried out only if the data misfit is deemed to be unsatisfactory. We demonstrate our approach using both synthetic and field examples. The field example involves waterflood history matching of a structurally complex and faulted offshore turbiditic oil reservoir. Permeability and fault transmissibilities are the main uncertainties. The geologic model consists of more than 800,000 cells and 10 years of production data from 8 producing wells. Using our dual scale approach, we are able to obtain a satisfactory history match with a finite-difference model in less than a day in a PC. Compared to a manual history matching, the dual scale approach is shown to better preserve the geological features and the pay/non-pay juxtapositions in the original geologic model.

Effect of Scaleup and Aggregation on the Use of Well Tests To Identify Geological Properties

Summary This paper discusses specific issues encountered when pressure tests are analyzed in reservoirs with complex geological properties. These issues relate to questions concerning the methodology of scaleup, the degree of aggregation, and the reliability of conventional methods of analysis. The paper shows that if we desire to use pressure-transient analysis to determine more complex geological features such as connectivity and widths of channels, we need a model that incorporates reservoir heterogeneity. This complexity can lead to significantly more computational effort in the analysis of the pressure transient. The paper demonstrates that scaleup criteria, based on steady-state procedures, are inadequate to capture transient pressure responses. Furthermore, the number of layers needed to match the transient response may be significantly greater than the number of layers needed for a reservoir-simulation study. The use of models without a sufficient number of layers may lead to interpretations that are in significant error. The paper compares various vertical aggregation methods to coarsen the fine-grid model. The pressure-derivative curve is used as a measure of evaluating the adequacy of the scaleup procedure. Neither the use of permeability at a wellbore nor the average layer permeability as criteria for the aggregation was adequate to reduce the number of layers significantly. Introduction The objectives of this paper are to demonstrate the impact of the detailed and small-scale heterogeneities of a formation on the flow characteristics that are obtained from a pressure test and how those heterogeneities affect the analysis of the pressure test. The literature recognizes that special scaleup procedures are required in the vicinity of wells located in heterogeneous fields. Our work demonstrates that these procedures apply only to rather small changes in pressure over time and are usually inadequate to meet objectives for history-matching well tests. Using a fine-scale geological model derived by geological and geophysical techniques, this work systematically examines the interpretations obtained by various aggregation and scaleup techniques. We will demonstrate that unless care is taken, the consequences of too much aggregation may lead to significant errors on decisions concerning the value of a reservoir. Current scaleup techniques presume that spatial (location of boundaries, location of faults, etc.) variables are maintained. In analyzing a well test, however, one of our principal objectives is to determine the relationship between the well response and geometrical variables. We show that a limited amount of aggregation will preserve the spatial and petrophysical relationships we wish to determine. At this time, there appears to be no method available to determine the degree of scaleup a priori. Because the objective of well testing is to estimate reservoir properties, the scaleup process needs to be made a part of the history-matching procedure. By assuming a truth case, we show that too much vertical aggregation may lead to significant errors. Comparisons with traditional analyses based on analytical techniques are made. Whenever an analytical model is used in the analysis, unless otherwise stated, we use a single-layer-reservoir solution.

Multiresolution Wavelet Scale Up of Unstable Miscible Displacements in Flow Through Heterogeneous Porous Media

We describe scale up of geological models of field-scale porous media using a new method based on the wavelet transformations. The porous media of interest contain broadly-distributed and correlated permeabilities. Wavelet transformation of the permeability field of such porous media coarsens the geological model from smallest to largest length scales, drastically reduces the number of equations to be solved, preserves the important information on the permeability field at all the relevant length scales, and yields numerical results for any fluid flow property that are as accurate as those that are obtained with the highly detailed geological model of the same porous media. To test this method, we carry out extensive computer simulations of unstable miscible displacement processes and the associated viscous fingering phenomenon in highly heterogeneous porous media, both with the fine-scale geological model and the coarsened model. Excellent agreement is found between the results of the two sets of simulations.

Applications of Streamline Simulations to Reservoir Studies

Summary Computer models of oil reservoirs have become increasingly complex in order to represent geological reality and its impact on fluid flow. Memory and CPU time limitations by finite-difference (FD)/ finite-volume (FV) simulators force a coarser resolution of reservoir models through upscaling. Upscaling can lead to significant difficulties in reservoir studies: (1) while the fine-scale geological model is built from petrophysical, log, and seismic data, its dynamic behavior is never checked. As a result, a coarse-scale reservoir study can be linked to a fine-scale geological model, but the two might be inconsistent in their dynamic behavior. (2) Conversely, the upscaled model cannot be properly tested because the flow and production behavior at the fine-scale level are not available. There is no reference solution for guiding important decisions for building a consistent upscaled model. (3) A large number of sector models are required in designing optimal well patterns. Streamline simulation is now an attractive alternative to overcome some of these drawbacks because it offers substantial computational efficiency while minimizing numerical diffusion and grid-orientation effects. It allows the integration of fine-scale geological models into the reservoir engineering workflow. In this paper, we demonstrate the usefulness and efficiency of a streamline simulator in the reservoir engineering workflow. We evaluate its speed, memory requirements, and scalability using tracer and black-oil-test data sets on an SGI Origin 2000™* (250 MHz MIPS). Our data are based on real fields and range from 200,000 to 7 million cells, with cells as small as 30×30×0.5 m. Streamlines allowed us to check the validity of a large geological model and to optimize well patterns with more than 30 producers and injectors. We demonstrate how streamline-based simulation has matured from a research tool to an industrial application providing real benefits to engineers as a complementary tool to existing conventional simulation technology based on FVs.