On the uncertainty of interwell connectivity estimations from the capacitance-resistance model

The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description. There is no prior geologic model. The principal output of CRM fitting is the fraction of injected fluid （usually water） that is produced at a producer at steady-state. These fractions are interwell connectivities. Interwell connectivities are fundamental information needed to manage waterfloods in oil reservoirs. The data-driven CRM is a fast tool to estimate these parameters in mature fields and allows one to make full use of the dynamic data available. This paper considers the problem of setting an upper bound on the uncertainty of interwell connectivities for linear-constrained models. Using analytical bounds and numerical simulations, we derive a consistent upper limit on the uncertainty of interwell connections that can be used to quantify the information content of a given dataset.

Prediction of immiscible gas flooding performance:a modified capacitance-resistance model and sensitivity analysis

Reservoir performance prediction is one of the main steps during a field development plan.Due to the complexity and time-consuming aspects of numerical simulators,it is helpful to develop analytical tools for a rapid primary analysis.The capacitance-resistance model(CRM)is a simple technique for reservoir management and optimization.This method is an advanced time-dependent material balance equation which is combined with a productivity equation.CRM uses production/injection data and bottom-hole pressure as inputs to build a reliable model,which is then combined with the oil-cut model and converted to a predictive tool.CRM has been studied thoroughly for water flooding projects.In this study,a modified model for gas flooding systems based on gas density and average reservoir pressure is developed.A detailed procedure is described in a synthetic reservoir model using a genetic algorithm.Then,a streamline simulation is implemented for validation of the results.The results show that the proposed model is able to calculate interwell connectivity parameters and oil production rates.Moreover,a sensitivity analysis is carried out to investigate effects of drawdown pressure and gas PVT properties on the new model.Finally,acceptable ranges of input data and limitations of the model are comprehensively discussed.

Input signal design to estimate interwell connectivities in mature fields from the capacitance-resistance model

Interwell connectivities are fundamental parameters required to manage waterfloods in oil reservoirs. Data-driven models, such as the capacitance-resistance model （CRM）, are fast tools to estimate these parameters from time-correlations of input （injection rates） and output （production rates） signals. Noise and structure of the input time-series impose limits on the information that can be extracted from a given data-set. This work uses the CRM to study general prescriptions for the design of input signals that enhance the information content of injection/production data in the estimation of well-to-well interactions. Numerical schemes and general features of the optimal input signal strategy are derived for this problem.

Insights into wettability alteration during low-salinity water flooding by capacitance-resistance model

The capacitance-resistance model(CRM)has been widely implemented to model and optimise water-flooding and enhanced oil recovery(EOR)techniques.However,there is a gap in the application of CRM to analyse physical phenomena in porous media as well as the performance of EOR methods,such as low-salinity water(LSW)flooding.The main purposes of this study were to investigate how changes in time constant,as a CRM parameter,can represent physical phenomena in porous media such as wettability alteration.Moreover,to show CRM is a reliable tool to use for interpretation of LSW process as an EOR method.The results of different experimental/modelling studies in this research showed that in CRM model time constant increases when the wettability alters to a water wetness state,whereby the smallest time constant value is observed for the oil wet medium and the highest is observed for the water wet medium.The cases with a gradual alteration in wettability show an increasing trend with the dilution of the injection water.The core flooding data confirms the observed results of the simulation approach.The increment in time constant values indicates the resistance against displacing fluid,which is due to the wettability alteration of the porous medium,resulting in additional oil production.The observations made during this research illustrate that the time constant parameter can be a powerful tool for comparing different EOR techniques,since it is a good indication of the speed of impact of a particular injection fluid on production.

Kriging-boosted CR modeling for prompt infill drilling optimization

The capacitance-resistance model(CRM)has been a useful physics-based tool for obtaining production forecasts for decades.However,the model's limitations make it difficult to work with real field cases,where a lot of various events happen.Such events often include new well commissioning(NWC).We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching.Moreover,it can be used for selecting a new well placement during infill drilling.To make the workflow even more versatile,an improved version of CRM was used.It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients.By preliminary re-weighing and interpolating these coefficients using kriging,the coefficients for potential wells can be determined.The approach was validated using both synthetic and real datasets,from which the cases of putting new wells into operation were selected.The workflow allows a fast assessment of future well performance with a minimal set of reservoir data.This way,a lot of well placement scenarios can be considered,and the best ones could be chosen for more detailed studies.