History matching under training-image based geological model constraints

History matching forms an integral part of the reservoir modeling work-flow process. Despite the existence of many history matching tools, the integration of production data with seismic and geological continuity data remains a challenge. Geostatistical tools exists for integrating large scale seismic and fine scale well/core data. A general framework for integrating production data with diverse types of geological/structural data is largely lacking. In this paper we develop a new method for history matching that can account for production data constraint by prior geological data, such as the presence of channels, fractures or shale lenses. With multiple-point (mp) geostatistics prior information about geological patterns is carried by training images from which geological structures are borrowed then anchored to the subsurface data. A simple Markov chain iteratively modifies the mp geostatistical realizations until history match. The method is simple and general in the sense that the procedure can be applied to any type of geological environment without requiring a modification of the algorithm.