Use of water chemistry data in history matching of a reservoir model

Daniel Arnold, Oscar Vazquez, Vasily Demyanov, Michael Andrew Christie

    Research output: Contribution to conferencePaper


    Produced Water Chemistry (PWC) has been included in the history matching of reservoir simulations. Generally, in conventional history matching, the water chemistry is not considered as an extra constraint. The chemistry of the different types of water in a reservoir, such as aquifer, connate and seawater is very different, and can be traceable. Produced Water Chemistry is the main source of information to monitor scale precipitation in oil field operations.

    The objective of this paper is to evaluate the effect of adding produced water chemistry information as an extra constraint history matching a modified version of the PUNQ-S3 reservoir model. The PUNQ-S3 model is a synthetic benchmark case that has been used previously for history matching and uncertainty quantification. Conventional historical production data (gas, oil rates and pressure) from six production wells are supported by the water chemistry tracer data from the wells that produce water in the history period. The different types of water are traced through their distinctive chemistries, namely aquifer, connate (formation) and sea (injection) water. Geological model is matched by varying porosity and permeability, both horizontal (kh) and vertical (kv) according to the prior beliefs about the reservoir geology (layering, spatial correlation and anisotropy).

    Two history matching (HM) scenarios are considered: including and not-including the Produced Water Chemistry (PWC) as an extra matching constraint. Stochastic Particle Swarm Optimization (PSO) algorithm is used to generate ensembles of history matched models, which characterise the uncertainty of the reservoir prediction. Comparison of the two scenarios reveals potential value of adding PWC data in history matching that allows achieving better matched models and ensuring diversity of HM models, which is essential for robust uncertainty quantification of the predictions.
    Original languageEnglish
    Number of pages13
    Publication statusPublished - Jun 2012
    EventSPE Europec/EAGE Annual Conference - Copenhagen, Denmark
    Duration: 4 Jun 20127 Jun 2012


    ConferenceSPE Europec/EAGE Annual Conference


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