High-dimensional geostatistical history matching Vectorial multi-objective geostatistical history matching of oil reservoirs and uncertainty assessment

• Published in: Computational geosciences Vol. 22; no. 2; pp. 607 - 622
• Main Authors: Carneiro, João, Azevedo, Leonardo, Pereira, Maria
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2018
• Subjects: Earth and Environmental Science; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Mathematical Modeling and Industrial Mathematics; Original Paper; Soil Science & Conservation; Uncertainty assessment; History matching; 86A32; Geostatistics; Multi-objective optimisation
• ISSN: 1420-0597; 1573-1499
• DOI: 10.1007/s10596-017-9712-6

Hydrocarbon reservoir modelling and characterisation is a challenging subject within the oil and gas industry due to the lack of well data and the natural heterogeneities of the Earth’s subsurface. Integrating historical production data into the geo-modelling workflow, commonly designated by history matching, allows better reservoir characterisation and the possibility of predicting the reservoir behaviour. We present herein a geostatistical-based multi-objective history matching methodology. It starts with the generation of an initial ensemble of the subsurface petrophysical property of interest through stochastic sequential simulation. Each model is then ranked according the match between its dynamic response, after fluid flow simulation, and the observed available historical production data. This enables building regionalised Pareto fronts and the definition of a large ensemble of optimal subsurface Earth models that fit all the observed production data without compromising the exploration of the uncertainty space. The proposed geostatistical multi-objective history matching technique is successfully implemented in a benchmark synthetic reservoir dataset, the PUNQ-S3, where 12 objectives are targeted.