Hidden information in ill-posed inverse problems

It is well known that parameter updating of large-scale numerical reservoir flow models (a.k.a. ‘computer assisted history matching’) is an ill-posed inverse problem. Typically the number of uncertain parameters in a reservoir flow model is very large whereas the available information for estimating these parameters is limited. The classic...

Quantification of the impact of data in reservoir modeling

Global energy use is increasing. As societies advance, they will continue to need energy to power residential and commercial buildings, in the industrial sector, for transportation and other vital services. To satisfy this rising demand, liquid, natural gas, coal, nuclear power and renewable fuel sources are extensively developed. Particularly...

Model-reduced gradient-based history matching

Since the world's energy demand increases every year, the oil & gas industry makes a continuous effort to improve fossil fuel recovery. Physics-based petroleum reservoir modeling and closed-loop model-based reservoir management concept can play an important role here. In this concept measured data are used to improve the geological model, while...

Model-reduced gradient-based history matching

Gradient-based history matching algorithms can be used to adapt the uncertain parameters in a reservoir model using production data. They require, however, the implementation of an adjoint model to compute the gradients, which is usually an enormous programming effort. We propose a new approach to gradient-based history matching which is based...

Hydrocarbon Reservoir Parameter Estimation Using Production Data and Time-Lapse Seismic

The numerical simulation of hydrocarbon reservoir flow is necessarily an approximation of the flow in the real reservoir. The knowledge about the reservoir is limited and some of the processes occurring are either not taken into account or not described in an adequate way. The parameters influencing the flow are usually not known, except in a...