3D Reservoir Geological Modeling

One of the data types transformed from seismic to modeling software is the polygon. Fault model is generated based on the seismic interpretation that is transferred in the form of polygons. In this video, user will see how polygons editing can be achieved to make fault modeling.

Horizoning process is the generation of reservoir top structure by considering various data constraining reservoir surface such as well tops, faults and seismic structure.

After defining subzones, vertical heterogeneity needs to be captured by further fine layers. Generally log resolution of 1 foot thickness is targeted and subzones are divided into layers with a thickness around 1 foot.

After framework modeling data are prepared, for the reservoir model, grids (cells) are generated with proper geological orientation, size and shape. Guidelines of geological understanding and geologist feedback should be fully incorporated in 3D model grid definition.

This video explains procedure and the data needed for 3D grid generation in Petrel.

Sometimes, geometric parameters are needed to be generated in geocellular model such as cell thickness, height above the contact and so forth.

Dynamic behaviour of reservoir depends very much rock distribution. Focus on facies change is given to core analysis and rock properties from open hole logs in addition to MICP analysis. A comprehensive approach is needed to integrate various data type available in a right workflow.

3D petrophysical properties can be generated using different algorithms including 3D trends, seismic data and RRT models as appropriate. Various source of information including drilling, core data, geological interpretation, seismic data, petrophysical information are employed to generate accurate dynamic characteristics of the reservoir.

Unfortunately, data is often not processed in a timely manner to extract the necessary information for the management and experts to make decisions. Furthermore, the available data is archived in various locations, in different formats, on different hardware and software platforms. Moreover the results of automated algorithms always needs to be cheked. Both the data and results of each modeling steps are QCed for consistency and validated for the integration.

Throughout the life of a hydrocarbon field, significant number of important decisions, such as recovery mechanism, and surface facilities, are made depending on incomplete and uncertain information. Uncertainty assessment is necessary to evaluate risk and make consistent decisions. An integrated method of uncertainty assessment enables us to manage static and dynamic parameters.

The construction of the 3D static models incorporating all the available data allows re-calculation of the volumetrics of the reservoir. Comparison to earlier figures and dynamic simulation results would be a validation factor for the model.

Some of the learning points are discussed regarding data versus model result. Analysis and solution methodologies are given for problems mentioned in this presentation.