Streamline-Based Rapid History Matching of Bottomhole Pressure and Three-Phase Production Data

Author(s):  
Dongjae Kam ◽  
Jichao Han ◽  
Akhil Datta-Gupta
2009 ◽  
Vol 12 (04) ◽  
pp. 528-541 ◽  
Author(s):  
Adedayo Oyerinde ◽  
Akhil Datta-Gupta ◽  
William J. Milliken

Summary Streamline-based assisted and automatic history matching techniques have shown great potential in reconciling high resolution geologic models to production data. However, a major drawback of these approaches has been incompressibility or slight compressibility assumptions that have limited applications to two-phase water/oil displacements only. Recent generalization of streamline models to compressible flow has greatly expanded the scope and applicability of streamline-based history matching, in particular for three-phase flow. In our previous work, we calibrated geologic models to production data by matching the water cut (WCT) and gas/oil ratio (GOR) using the generalized travel-time inversion (GTTI) technique. For field applications, however, the highly nonmonotonic profile of the GOR data often presents a challenge to this technique. In this work we present a transformation of the field production data that makes it more amenable to GTTI. Further, we generalize the approach to incorporate bottomhole flowing pressure during three-phase history matching. We examine the practical feasibility of the method using a field-scale synthetic example (SPE-9 comparative study) and a field application. The field case is a highly faulted, west-African reservoir with an underlying aquifer. The reservoir is produced under depletion with three producers, and over thirty years of production history. The simulation model has several pressure/volume/temperature (PVT) and special core analysis (SCAL) regions and more than 100,000 cells. The GTTI is shown to be robust because of its quasilinear properties as demonstrated by the WCT and GOR match for a period of 30 years of production history.


SPE Journal ◽  
2003 ◽  
Vol 8 (04) ◽  
pp. 328-340 ◽  
Author(s):  
Ruijian Li ◽  
A.C. Reynolds ◽  
D.S. Oliver

2016 ◽  
Vol 19 (04) ◽  
pp. 683-693 ◽  
Author(s):  
Zhaoqi Fan ◽  
Yin Zhang ◽  
Daoyong Yang

Summary In this paper, a modified ensemble randomized maximum-likelihood (EnRML) algorithm has been developed to estimate three-phase relative permeabilities with consideration of the hysteresis effect by reproducing the actual production data. Ensemble-based history matching uses an ensemble of realizations to construct Monte Carlo approximations of the mean and covariance of the model variables, which can acquire the gradient information from the correlation provided by the ensemble. A power-law model is first used to represent the three-phase relative permeabilities, the coefficients of which can be automatically adjusted until production history is matched. A damping factor is introduced as an adjustment to the step length because a reduced step length is commonly required if an inverse problem is sufficiently nonlinear. A recursive approach for determining the damping factor has been developed to reduce the number of iterations and the computational load of the EnRML algorithm. The restart of reservoir simulations for reducing the cost of reservoir simulations is of significant importance for the EnRML algorithm where iterations are inevitable. By comparing a direct-restart method and an indirect-restart method for numerical simulations, we optimize the restart method used for a specific problem. Subsequently, we validate the proposed methodology by use of a synthetic water-alternating-gas (WAG) displacement experiment and then extend it to match laboratory experiments. The proposed technique has proved to efficiently determine the three-phase relative permeabilities for the WAG processes with consideration of the hysteresis effect, whereas history-matching results are gradually improved as more production data are taken into account. The synthetic scenarios demonstrate that the recursive approach saves 33.7% of the computational expense compared with the trial-and-error method when the maximum iteration is 14. Also, the consistency between the production data and model variables has been well-maintained during the updating processes by use of the direct-restart method, whereas the indirect-restart method fails to minimize the uncertainties associated with the model variables representing three-phase relative permeabilities.


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