Assisted History Matching for Surface-Coupled Gas-Reservoir Simulation

Abstract

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 101233, "Assisted History Matching for Surface-Coupled Gas-Reservoir Simulation," by D. Biswas, SPE, SiteLark, prepared for the 2006 SPE Asia Pacific Oil and Gas Conference and Exhibition, Adelaide, Australia, 11–13 September. The delivery-point pressures for gas reservoirs are known, and operators are obligated to supply gas into those downstream pressures. Therefore, reservoir-deliverability prediction must account for the pressure drop in the surface network. Also, this coupled system should honor historical attributes of pressures and rates. In this study, a modified Gauss-Newton method was used in conjunction with a nonlinear parameter-estimation algorithm to history match a surface/reservoir-coupled gas-reservoir simulation. Introduction A control-volume finite-element based reservoir simulator can use unstructured grids to analyze near-wellbore and high-activity flow areas (i.e., high-permeability channels and fractures). The pressure drop in the surface network is modeled with Weymouth's equation of steady-state pipeline flow. More importantly, the total system is solved in a coupled fashion, thereby enabling control of the solution with surface-pressure constraints. In addition, both surface and reservoir decision variables are estimated with a modified Gauss Newton algorithm in the assisted history-matching step. Background Previously published methods investigate and apply various numerical experiments of coupling reservoir and surface models. Coupling explicit vs. implicit techniques, full-system-solve vs. domain decomposition, and applications to large fields to optimize production and maximize surface-facility use have been examined extensively. However, time-consuming calibration to known (observed) data points (i.e., history-matching process) is not emphasized. Much time and effort is spent to history match and condition reservoir models to dynamic data. However, past performance of history-matched predictions has been erratic for several reasons. In brute-force history-matching procedures, the reservoir model is adjusted in an ad hoc fashion until the available production-history data are matched. In the process, the model may cease to be consistent with the prior geological model. History-matching algorithms presume that the deviation of the predicted response from the observations is attributable to only a small set of reservoir variables, such as permeability or porosity. Actually, the production response is affected by many variables (e.g., relative permeability, capillary pressure, and fractures). Uncertainty in reservoir-parameter distribution and the influence on the production response should be considered in more detail. The trend has been to start with a single reservoir model and perform history matching to obtain a single perturbed reservoir model. The history-matched model is not unique, and a suite of models is possible, each satisfying the available production data. To represent uncertainty in predicted response realistically, the predictions obtained from several history-matched realizations must be aggregated. Therefore, a new method was proposed in which the underlying heterogeneity was conditioned to the production response. The proposed approach focuses predominantly on gas reservoirs for which the number of decision variables (parameters) is very low and benefits of cost-effective history matching are high. History matching with this reduced variable set could reduce inaccuracies in the predictions for future performance.