Abstract
One of the main concern in the O&G business is generating reliable production profile forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow 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 direct problem building workflow.
Using the Bayesian framework, a posterior distribution of the most sensitive parameters are derived from the a priori distributions and a non-linear proxy model of the likelihood function. The later is computed using experimental design, kriging and dynamic training techniques.
Multiple History Matched models together with a posteriori parameter distributions are finally used in a joint modeling approach to capture the main uncertainties and to obtain typical (P10-P90) probabilistic production profiles.
This workflow has been applied to a gas storage real case submitted to significant seasonal pressure variations. Probabilistic operational pressure profiles for a given period can then be compared to the actual gas storage dynamic behaviour to assess the added value of the proposed workflow.
