Using Experimental Designs, Assisted History Matching Tools and Bayesian Framework to Get Probabilistic Production Forecasts (SPE-113498)

摘要

One of the main concerns in the O&G business is generating reliable production profile forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions.Aworkflow combining different methodologies to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable production forecasts is proposed. Using experimental design theory, a sensitivity study is first performed to scan the whole range of static and dynamic uncertain parameters using a proxy-model of the fluid flow simulator. Only the most sensitive ones with respect to an objective function (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps. Assisted History Matching tools are then used to get multiple History matched models an order of magnitude faster than traditional History Matching processes. Updated uncertain parameters (selected from the sensitivity studies) may be picked anywhere in the 'geomodeling to simulation' workflow. Using the Bayesian framework, a posterior distribution of the most sensitive parameters are derived from the a priori distributions and a proxy model of the likelihood function. The later is computed using experimental design and non linear regression techniques. Multiple History Matched models together with a posterior parameter distribution are finally used in a joint modelling approach to capture the main uncertainties and to obtain typical (