No abstract

One of the challenging issues of using 4D seismic data into reservoir history matching is to compare the measured data to the model data in a consistent way. It is important to decide which kind of seismic data can be best used and at which level of history matching process they can be integrated. In this work, we have performed 4D seismic history matching of a sector model based on a North sea reservoir in the ensemble Kalman filter (EnKF) framework and have investigated the effects of different types of time-difference seismic data to update reservoir models. The reservoir-seismic model system consists of a commercial reservoir simulator coupled to an implemented rock physics model and a forward seismic modeling tool based on 1D convolution with weak contrast reflectivity approximation. The objective of this work is to investigate the sensitivity of different combinations of production and seismic data on EnKF model updating. The uncertain static reservoir parameters considered are porosity and permeability; dynamic variables such as pressure and water saturation are conditioned to both production and seismic data. In particular, we are interested to quantify the performance of the wells; also to match seismic data and to estimate the reservoir parameters. In most of the cases of reservoir characterization, time-difference impedance data performed better than time-difference amplitude data and considerably reduced posterior ensemble spread. The matching of seismic data generally improved with the inclusion of time-difference seismic data. In estimating posterior porosity and permeability, seismic difference data provided better estimate than using only production data, especially in aquifer region and also in areas that might be considered for in-fill wells. Thus, in our realistic synthetic case based on a full field reservoir model, we experienced that the integration of seismic data in the elastic domain provided better performance than using amplitude data. In addition, we investigated the effects of vertical resolution of seismic data on EnKF model updating, and showed that the choice of wavelet discretization points can have significant influence on the quality of history matching.

History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results and consequences for the uncertainty quantification are presented. Introduction In recent years there has been more and more attention given to workflows for uncertainty assessment in reservoir management. Structured approaches exist for assessing the impacts of uncertainty on investment decision-making in the oil and gas industry.1 These approaches mostly rely on simplified component models for each decision domain such as G&G models, production scenarios, drilling models, processing facilities, economics and related costs. Because of its complexity, the integration of dynamical modelling is only gradually entering this domain of decision-making processes. It is generally accepted that any model reliably predicting future quantities should be able to reproduce known history data. This requires a model validation process2 (History Matching) which is traditionally cumbersome and time consuming. The consistent inclusion of production data calls for computation-intensive processing. This requires new approaches in the application of experimental design and optimization methods which is supported by the use of high-performance computing facilities3,4 . One crucial change in the mind set within reservoir engineering is the accepted importance of including multiple realizations of dynamical reservoir models into the forward modelling process to account for uncertainties in the prediction phase.5,6 Although several studies on deriving multiple solutions to a History Matching problem6–9 have been published recently there are nevertheless no structured workflows or optimization methods applicable to reservoir simulation which address the problem of multi-objective optimization MOO.10–13 Recent analysis of the TDRM6 workflow within the context of History Matching has identified single-objective optimization techniques as one weakness vis-à-vis manual history matching.7 This work extends the application of MOOs which are already applied in other industry areas and focuses on an implementation of a multi-objective optimization technique with applications in reservoir simulation. Multi-objective optimization criteria in reservoir simulation are not just addressing the problem statement of History Matching. Other examples include production optimization, portfolio optimization, etc. The methodology used in this work is described in the next chapter. Readers interested only in principal capabilities of the multi-objective optimization technique and results should refer to the implementation chapter and the application to History Matching.

Quantitative incorporation of 4D seismic data into reservoir history matching is an attractive proposition. The use of ensemble Kalman filter (EnKF) in this regard improves overall quality of seismic data matching and estimating reservoir parameters. We performed 4D seismic history matching of a sector model based on a North sea reservoir using real inverted 4D impedance data. Special challenges are involved when we assimilate large amount of in EnKF, and hence, a methodology based on a combination of global and local analysis scheme is used. In history matching process, we have focused on matching the acoustic impedance ratio between two time steps of several years of production. A petro-elastic modeling based on rock physics recipe of North sea reservoir is used. Uncertain reservoir parameters considered are porosity and permeability. Updated dynamic variables (pressure and water saturation) are conditioned to both production and seismic data. The large number of measurements introduced by the integration of 4D seismic data is handled by an efficient EnKF scheme with the possibility to perform localization. Global and local analysis schemes assimilate production data and seismic data respectively. In our implementation of local analysis, we used three significant regions based on flow conditions and seismic data within a given local analysis region is influenced by only variables in the same region. The posterior ensemble of models showed good match to both production data and seismic data. In most of the cases of reservoir characterization, the combined use of 4D seismic with production data improved history matching for the wells and also improved posterior impedance ratio data matching. In addition, 4D seismic data provided more information related to permeability update in the aquifer and in-fill areas. The results indicate that the proposed local analysis-based model updating scheme reduced the amount of spurious correlations and tendencies to ensemble collapse seen with global analysis.

One of the challenging issues of using 4D seismic data into reservoir history matching is to compare the measured data to the model data in a consistent way. It is important to decide which kind of seismic data can be best used and at which level of history matching process they can be integrated. In this work, we have performed 4D seismic history matching of a sector model based on a North sea reservoir in the ensemble Kalman filter (EnKF) framework and have investigated the effects of different types of time-difference seismic data to update reservoir models. The reservoir-seismic model system consists of a commercial reservoir simulator coupled to an implemented rock physics model and a forward seismic modeling tool based on 1D convolution with weak contrast reflectivity approximation. The objective of this work is to investigate the sensitivity of different combinations of production and seismic data on EnKF model updating. The uncertain static reservoir parameters considered are porosity and permeability; dynamic variables such as pressure and water saturation are conditioned to both production and seismic data. In particular, we are interested to quantify the performance of the wells; also to match seismic data and to estimate the reservoir parameters. In most of the cases of reservoir characterization, time-difference impedance data performed better than time-difference amplitude data and considerably reduced posterior ensemble spread. The matching of seismic data generally improved with the inclusion of time-difference seismic data. In estimating posterior porosity and permeability, seismic difference data provided better estimate than using only production data, especially in aquifer region and also in areas that might be considered for in-fill wells. Thus, in our realistic synthetic case based on a full field reservoir model, we experienced that the integration of seismic data in the elastic domain provided better performance than using amplitude data. In addition, we investigated the effects of vertical resolution of seismic data on EnKF model updating, and showed that the choice of wavelet discretization points can have significant influence on the quality of history matching.