A Probabilistic Integration of Well Log, Geological Information, 3D/4D Seismic, and Production Data: Application to the Oseberg Field
- Scarlet Castro (Stanford University) | Jef Karel Caers (Stanford University) | Cecilie Otterlei (Norsk Hydro) | Trond Andersen (Norsk Hydro) | Trond Hoye (Hydro) | Perrine Gomel (Norsk Hydro)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-27 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2006. Society of Petroleum Engineers
- 2.4.3 Sand/Solids Control, 5.5.3 Scaling Methods, 5.1.1 Exploration, Development, Structural Geology, 4.3.4 Scale, 6.1.5 Human Resources, Competence and Training, 5.4.2 Gas Injection Methods, 5.1.9 Four-Dimensional and Four-Component Seismic, 7.6.2 Data Integration, 5.6.1 Open hole/cased hole log analysis, 5.1.5 Geologic Modeling, 5.5.8 History Matching, 3.3 Well & Reservoir Surveillance and Monitoring, 5.1 Reservoir Characterisation
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The ultimate goal of reservoir modeling is to obtain a model of the reservoir that is able to predict future flow performance. Achieving this challenging goal requires the model to honor all available static (well log, geological information and 3D seismic) and dynamic (4D seismic and production) data. This paper introduces a general methodology and workflow for reservoir modeling that integrates data from multiple and diverse sources, using a probabilistic approach addressing the possible inconsistency and/or redundancy between various data sources.
The workflow is evaluated in the Oseberg Field, located in the Norwegian sector of the North Sea, 140 Km west of Bergen. The focus of this study is the Upper Ness Formation in the Alpha North structural segment of the field. The main contribution of this paper is the inclusion of 4D seismic data, which has previously not been accounted for within this workflow.
The reservoir modeling workflow applied to this case study helped modeling the spatial distribution of the channel facies using a multiple-point geostatistical technique while honoring all available data and explicitly accounting for data redundancy.
Creating a model of the reservoir is becoming a common practice during several stages of the reservoir life. From exploration to field abandonment, reservoir modeling pursues the general goal of understanding and predicting important geological, geophysical and engineering components of the reservoir.
Reservoir modeling calls for the integration of expertise from different disciplines, as well as the integration of data from various sources. Each type of data provides information about the reservoir heterogeneity on a different scale; therefore, they have different degrees of accuracy and may be redundant towards modeling the reservoir. The reservoir model needs to simultaneously (not hierarchically) honor all available data, both static (well-log, geological information and 3D seismic) and dynamic (4D seismic and production), in order to preserve its predictive capabilities.
An approach to integrate static and dynamic data (production and 4D seismic data) for reservoir characterization has
been evaluated by Kretz et al (2002) and Mezghani et al (2004), simultaneously integrating different sources of information using an optimization methodology based on the gradual deformation method. In this paper we rely on a probabilistic scheme for data integration of well log, geological information 3D/4D seismic and production data, within the probability perturbation method (Caers, 2003).
A fully probabilistic methodology and workflow for reservoir modeling that integrates data from multiple and diverse
sources is proposed, using an approach that explicitly addresses the possible inconsistency and/or redundancy between various data sources (Hoffman, 2005). The general goal of the workflow is to model an unknown (facies or petrophysical property) using data from different sources. The information content of each data source is modeled as a spatial probability distribution model; using Journel's tau model (Journel, 2002) these individual spatial probabilities are combined into a simple joint conditional probability from which reservoir models are drawn using
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