Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problem...Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.展开更多
Gas flooding such as CO2 flooding may be effectively applied to ultra-low permeability reservoirs, but gas channeling is inevitable due to low viscosity and high mobility of gas and formation heterogeneity. In order t...Gas flooding such as CO2 flooding may be effectively applied to ultra-low permeability reservoirs, but gas channeling is inevitable due to low viscosity and high mobility of gas and formation heterogeneity. In order to mitigate or prevent gas channeling, ethylenediamine is chosen for permeability profile control. The reaction mechanism of ethylenediamine with CO2, injection performance, swept volume, and enhanced oil recovery were systematically evaluated. The reaction product of ethylenediamine and CO2 was a white solid or a light yellow viscous liquid, which would mitigate or prevent gas channeling. Also, ethylenediamine could be easily injected into ultra-low permeability cores at high temperature with protective ethanol slugs. The core was swept by injection of 0.3 PV ethylenediamine. Oil displacement tests performed on heterogeneous models with closed fractures, oil recovery was significantly enhanced with injection of ethylenediamine. Experimental results showed that using ethylenediamine to plug high permeability layers would provide a new research idea for the gas injection in fractured, heterogeneous and ultra-low permeability reservoirs. This technology has the potential to be widely applied in oilfields.展开更多
Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that...Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image(TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter(EnKF) and ensemble smoother(ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.展开更多
基金support from the Shandong Natural Science Foundation(Grant No.ZR2010EM053)the Fundamental Research Funds for the Central Universities(Grant No.10CX04042A)
文摘Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.
基金Financial support for this work from National Sciencetechnology Support Plan Projects (No. 2012BAC26B00)the Science Foundation of China University of Petroleum, Beijing (No.2462012KYJJ23)
文摘Gas flooding such as CO2 flooding may be effectively applied to ultra-low permeability reservoirs, but gas channeling is inevitable due to low viscosity and high mobility of gas and formation heterogeneity. In order to mitigate or prevent gas channeling, ethylenediamine is chosen for permeability profile control. The reaction mechanism of ethylenediamine with CO2, injection performance, swept volume, and enhanced oil recovery were systematically evaluated. The reaction product of ethylenediamine and CO2 was a white solid or a light yellow viscous liquid, which would mitigate or prevent gas channeling. Also, ethylenediamine could be easily injected into ultra-low permeability cores at high temperature with protective ethanol slugs. The core was swept by injection of 0.3 PV ethylenediamine. Oil displacement tests performed on heterogeneous models with closed fractures, oil recovery was significantly enhanced with injection of ethylenediamine. Experimental results showed that using ethylenediamine to plug high permeability layers would provide a new research idea for the gas injection in fractured, heterogeneous and ultra-low permeability reservoirs. This technology has the potential to be widely applied in oilfields.
基金supported by Korea Institute of Geoscience and Mineral Resources(Project No.GP2017-024)Ministry of Trade and Industry [Project No.NP2017-021(20172510102090)]funded by National Research Foundation of Korea(NRF)Grants(Nos.NRF-2017R1C1B5017767,NRF-2017K2A9A1A01092734)
文摘Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image(TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter(EnKF) and ensemble smoother(ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.
