A multidisciplinary approach that is maximizing information extraction from seismic to predict lithofacies and reservoir properties, based on the following steps is presented:
Multi-attribute seismic analysis was applied based on an unsupervised machine learning process called Self- Organizing Maps (SOMs) in Paradise software. The selection of input attributes was thoroughly tested and optimized, based on close co-operation between geophysicists and geologists to extract more extensive and detailed geological features from seismic.
Using the information from nearby wells and knowledge of rock physics, the individual neural classes were quantified and validated and then reorganized and translated to formation properties such as lithofacies, porosity, and clay content.
The study focused on the benefits and additional information that can be gained with this new approach compared to traditional quantitative interpretation approaches (i.e., a prediction from acoustic impedance alone). Multi-attribute classification using machine learning, SOM, gave a better representation of seismic characters, detecting the geologic trends in the field. A detailed quantitative interpretation of SOM neural classes was established to validate and translate formation-related classes optimally for reservoir prediction, and to eliminate classes irrelevant to the formations (i.e., seismic noise).
The result from the Wisting case study shows that the new method gives the best match to the well data and extracts more reservoir related information from seismic compared to the conventional quantitative interpretation (QI) approach. In the upper part of the Triassic, with fluvial sediments assigned to the RG2 unit (Fruholmen Fm.), the reservoir quality and extent of mud clast rich channel intervals are debated. In two of the wells (E and B), a thicker mud unit was observed, and it was debated if this could act as a barrier towards the overlying good reservoir. The result shows that the mud intervals are deposited locally and do not represent a regional mud layer. The additional information from seismic seems to be valuable when used as input and refinement to the digital geological model.
Principal Geophysicist – Idemitsu
Sharareh is currently working as principal geophysicist for Idemitsu Petroleum Norge (IPN). Sharareh Holds a BSc. In physics and MSc in geophysics. She has been working for 17 years as geophysicist and Quantitative Interpretation Specialist for Norsk Hydro, Statoil (currently known as Equinor) and Idemitsu Petroleum Norge. She has been engaged in various exploration projects in Brazil, Gulf of Mexico, offshore Canada, Nigeria, Angola, Tanzania, Mozambique and Norwegian Continental Shelf.