3D modeling in Petrel of geological CO2 storage site

University essay from Uppsala universitet/Institutionen för geovetenskaper


If mitigation measures are not made to prevent global warming the consequences of a continued global climate change, caused by the use of fossil fuels, may be severe. Carbon Capture and Storage (CCS) has been suggested as a way of decreasing the global atmospheric emission of CO2. In the realms of MUSTANG, a four year (2009-2013) large-scale integrating European project funded by the EU FP7, the objective is to gain understanding of the performance as well as to develop improved methods and models for characterizing so- called saline aquifers for geological storage of CO2. In this context a number of sites of different geological settings and geographical locations in Europe are also analyzed and modeled in order to gain a wide understanding of CO2 storage relevant site characteristics. The south Scania site is included into the study as one example site with data coming from previous geothermal and other investigations. The objective of the Master's thesis work presented herein was to construct a 3D model for the south Scania site by using modeling/simulation software Petrel, evaluate well log data as well as carry out stochastic simulations by using different geostatistical algorithms and evaluate the benefits in this. The aim was to produce a 3D model to be used for CO2 injection simulation purposes in the continuing work of the MUSTANG project.

The sequential Gaussian simulation algorithm was used in the porosity modeling process of the Arnager greensand aquifer with porosity data determined from neutron and gamma ray measurements. Five hundred realizations were averaged and an increasing porosity with depth was observed.


Two different algorithms were used for the facies modeling of the alternative multilayered trap, the truncated Gaussian simulation algorithm and the sequential indicator simulation algorithm. It was seen that realistic geological models were given when the truncated Gaussian simulation algorithm was used with a low-nugget variogram and a relatively large range.

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