The goal of the GEOPARD project is about bringing more geological realism into the statistical framework used to generate 3D models of geology. The aim is to make the models more realistic, easier to use and open up for direct use of outcrop data that is often overlooked.
The GEOPARD project aims to formulate geological rules in a statistical framework. This mimics the geological processes by describing the geometries and distribution of the rocks, without going into the details of the physical equations. By keeping everything within a statistical framework, we can use existing algorithms for handling data conditioning and will thus be able to identify those possible reservoirs that honour both the geological concept and the observed data.
Most petroleum reservoirs offshore Norway are built up of patches of sandstone filled with hydrocarbons that may flow when production starts. These sand stones or reservoir rocks, are surrounded by compact shale which acts as barriers to flow. In order to optimize production, it is important to have a good model for the distribution of the reservoir rocks and the potential barriers between them. This distribution is controlled by the geological processes that created the reservoir.
New models with more geological realism make sure that we can estimate values that will be used in the models directly from geological outcrop data already available in large databases. This will enable utilization of the data and its associated geological knowledge.
To succeed in this interdisciplinary project, we have joined the forces of experts within the fields of geoscience, statistics, geomodelling and software development, whom together with industry professionals form a resourceful team embarking on this research project to integrate geology and statistics.
This proposed project, referred to as GEOPARD, is about bringing more geological realism into the 3D subsurface models used by the Norwegian petroleum industry. The more than twenty year old technology commonly used today is long overdue for an upgrade, and the industry calls for a modern algorithm that can handle increasingly complex well patterns and ensure a realistic representation of the geology.
Our solution is to integrate geological rules into the core of the proven Bayesian statistical framework. A geological rule can be for example the stacking pattern of facies objects as a result of the depositional process. A rule-based approach will produce geologically meaningful predictions, allow for efficient testing of geological scenarios and increase the value of reservoir and analogue data. The dominant task is to develop and implement a new facies modelling algorithm that can be used by the petroleum industry in their reservoir management workflows, and research tasks will be focused on supporting this development.
The key challenge is to define a set of rules that balances geological realism with statistical consistency. We will utilize geological analogues to define and develop representative rules and objects. It is important that we manage to preserve geological realism in the presence of reservoir data. To support ease of use, we will implement an algorithm to estimate input parameters from interpreted analogue data stored in the SAFARI database.
To succeed in this interdisciplinary project, we have joined the forces of geoscience at University of Bergen and statistics at NTNU in collaboration with the modelling community at Norwegian Computing Center. The project involves science recruiting through two research fellowships. International collaboration is established through John Howell at University of Aberdeen, the project leader for SAFARI.