History matching under training-image-based geological model constraints

History matching forms an integral part of the reservoir modeling workflow 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 now are routinely employed 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 is proposed to iteratively modify the MP geostatistical realizations until history match. The method is simple and general in the sense that the procedure can be applied to a wide variety of geological environments without requiring a modification of the algorithm. The method does not make assumptions on the flow model. Synthetic cases are used to assess the flexibility of the proposed method.