Structured Approach for Probabilistic Assisted History Matching Using Evolutionary Algorithms: Tengiz Field
Authors: Denney Dennis 
Published: February 2015

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 116212, "A Structured Approach for Probabilistic Assisted History Matching Using Evolutionary Algorithms: Tengiz Field Applications," by H. Cheng, SPE, and K. Dehghani, SPE, Chevron Energy Technology Company, and T. Billiter, Tengizchevroil Kazakhstan, prepared for the 2008 SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21-24 September. The paper has not been peer reviewed.
Model calibration, or history matching, commonly has been conducted on a single deterministic model with a trial-and-error approach. Advances have been made in experimental design for use of numerical models in probabilistic forecasting. By use of evolutionary algorithms, a structured (staged) assisted history-matching method can be applied by considering probabilistic ranges of relevant input parameters and tailored objective functions for each stage of the process. A workflow was designed so that the ranges of selected parameters used to attain the best solutions at each stage could be carried over to the next stage to continue the history-match process.

Introduction
Numerical models are used to predict a range of ultimate recoveries and to appraise different development scenarios for oil and gas assets. The validity of reservoir models for performance prediction in brownfield assets is determined by their calibration against the historical production data.

History Match.
Model calibration has been conducted by changing regional and local reservoir properties to reconcile the model with observed production data. In manual history matching, a structured approach is used widely in which the sequence of scales of adjustments has been from global to flow units (regional), followed by local changes in model properties. In a geologically based automatic history matching, integrating production data typically requires minimization of a predefined data misfit and penalty terms to match the observed and calculated production response.
There are two general methods to assist history matching by derivative-free (or direct search) technologies. In one, optimization of the objective function relies mainly on proxies. In this approach, experimental design is used to generate a response surface to guide the history-matching process. The second method uses genetic algorithm (GA) and evolution strategy. In this application, proxies and evolutionary algorithms were used together to accelerate the history-match process.

Workflow Issues.
Probabilistic history matching by use of proxies from experimental design and evolutionary algorithms, combined with parallel computing, brought about new tools for achieving a range of possibilities for acceptable history-match solutions. The structured-approach workflow of manual history matching has not been applied in probabilistic history-matching techniques. The assisted-history-matching process using experimental design and stochastic methods can be conducted by adjusting all the parameters at the same time regardless of considering any hierarchy in scale.