STORM: Integrated 3D Stochastic Reservoir Modeling Tool for Geologists and Reservoir Engineers
- R.B. Bratvold (Statoil A/S) | Lars Holden (Statoil A/S) | Tarald Svanes (Statoil A/S) | Kelly Tyler (Statoil A/S)
- Document ID
- Society of Petroleum Engineers
- SPE Computer Applications
- Publication Date
- May 1995
- Document Type
- Journal Paper
- 58 - 67
- 1995. Society of Petroleum Engineers
- 5.5.5 Evaluation of uncertainties, 5.6.4 Drillstem/Well Testing, 4.3.4 Scale, 5.1.2 Faults and Fracture Characterisation, 5.1.8 Seismic Modelling, 4.1.5 Processing Equipment, 5.1 Reservoir Characterisation, 5.1.5 Geologic Modeling, 2.4.3 Sand/Solids Control, 5.5.3 Scaling Methods, 5.1.3 Sedimentology, 5.6.3 Deterministic Methods, 5.6.5 Tracers, 5.5.8 History Matching, 5.5 Reservoir Simulation, 4.1.2 Separation and Treating, 5.5.11 Formation Testing (e.g., Wireline, LWD), 3.3.1 Production Logging
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The petroleum industry is presently focusing on improved reservoir characterisation. Decisions concerning development and depletion of hydrocarbon reservoirs must be taken considering the uncertainties of the formation involved. This requires that geological and engineering data are combined to develop a detailed reservoir model.
Geostatistics and stochastic modeling techniques have emerged as promising approaches for integrating all relevant information and describing heterogeneous reservoirs. By using stochastic techniques to generate a range of equiprobable reservoir descriptions the uncertainty in the important reservoir parameters can be quantified. This quantification, together with the enhanced understanding of the reservoir characteristics given by stochastic reservoir modeling and visualization, provides an essential basis for making informed field development decisions.
Until now no widely accepted software system for stochastic reservoir characterisation has been available. This paper presents a software system, STORM, which integrates the different data sources with a stochastic approach for reservoir description.
As a result of high costs only a minimum of exploration and appraisal wells can be justified before important field development decisions are made. The use of oversimplified geological models based on data from a limited number of widely spaced wells is probably one of the most important reasons for the failures in predicting field performance. Oversimplification and the use of unrealistic geological models partly results from the paucity of well data but also results from the inappropriate use of available data. Experience has shown, for example, that linear interpolation of petrophysical characteristics between wells some kilometers apart usually will not give a realistic image of the heterogeneity required to predict fluid flow. To give a realistic description of the spatial variation, we resort to stochastic models and simulation. A large number of papers discussing geostatistics and stochastic modeling techniques have been published. Haldorsen and Damsleth present an excellent overview of the value of stochastic modeling as well as the methods suitable for reservoir description.
|File Size||5 MB||Number of Pages||10|