Application of Sector Modeling Technology for Giant Reservoir Simulations
- V. I. Dzyuba (TNK-BP) | Yu. V. Litvinenko (TNK-BP) | K. Yu. Bogachev (Rock Flow Dynamics) | A. R. Migrasimov (Rock Flow Dynamics) | A. E. Semenko (Rock Flow Dynamics) | E. A. Khachaturova (Rock Flow Dynamics) | Dmitry Eydinov (Rock Flow Dynamics)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition, 16-18 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 5.1.5 Geologic Modeling, 1.6.9 Coring, Fishing, 4.3.4 Scale, 2.2.2 Perforating, 5.5.8 History Matching, 5.5.3 Scaling Methods, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.1.1 Exploration, Development, Structural Geology, 5.5 Reservoir Simulation, 5.3.2 Multiphase Flow, 3 Production and Well Operations, 3.3.6 Integrated Modeling, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.3.1 Flow in Porous Media
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Currently, to build models for dynamic simulations of large oil and gas fields, engineers have to upscale the original geological grid not to exceed limits set but by computer memory size, CPU performance, and by the capabilities of standard commercial software packages. As a result, the upscaled model often has a rather low level of detalization. Since upscaling inevitably generates additional simulation errors, the resulting simulation model can solve only a limited number of practical tasks. The coarsened models are typically used for strategic tasks - to find an appropriate field development system, understand fluid and gas migration processes, and plan future production for this field.
Such upscaled models are rarely used for planning of risky, high cost and practically important tactical and operational tasks of field development management and production monitoring. It's ironic that oil and gas companies invest large sums of money on detalization of the reservoir geological description, and then have to drop this information in the process of hydrodynamic simulations. Engineers often call this paradox a "simulation scale problem". Since computer hardware performance increases exponentially in time, it is the technological level of software that becomes the main limitation factor.
If one could build a coherent "hardware+software" solution to resolve flow dynamics in porous media for geological grids with tens and hundreds of millions of blocks without upscaling, the problem could be solved.
In this article, the technology for constructing and effective handling of giant field models by application of sector modeling and advanced parallel algorithms is discussed. The important role of modern computer hardware architecture - especially processors and RAM designs is emphasized.
The authors discuss practical aspects concerning model dimensions, simulation model calculation speed for the whole field model or for any of its parts, choice of optimal model cut into sections, and boundary condition setting methods.
Technology application results are demonstrated for one of the world's biggest oil fields, with a geological model size of about 43 million grid blocks. The authors show that when the whole field is divided into a certain number of sector models, the sum of their calculation times may be substantially smaller than the full model calculation time. At the same time, if boundary conditions are included in simulations of subdomains, the spread in values of calculated production rates can be as small as 1%. The approach described in this paper appears to be efficient for history matching of large hydrodynamic models. It helps to reduce the time to completion for the project, and avoid the unnecessary modeling precision degradation caused by grid upscaling.
Large full field simulation model development is the most challenging and demanding task in field development planning. A thorough large field history match, in the case of hundreds and even thousands of wells and decades of historical production data is an almost unmanageable task nowadays. Simultaneous history matching of a large number of wells in the vast majority of cases can't be reached by global reservoir parameter changes, so local modifications should be used. At the same time, the well interference problem highly complicates the choice of history matching parameters and their simultaneous optimal value selection. So when working with such models, project teams have to restrict themselves to history matching of regions, well groups, or the reservoir as a whole. In such cases, even this result is not providing a reliable development forecast and requires many months of meticulous work.
Sector modeling can be one of the effective instruments for the solution to a large field history match. In this case, the large model is divided into sector models with an individual hydrodynamic model built for each of them. After history matching of each section, the sector models are merged back into the full field model.
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