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Collaborating Authors
Results
An Improved Inversion Workflow Jointly Assimilating 4D Seismic and Production Data
Jin, Long (Shell International E&P Inc.) | Gao, Guohua (Shell International E&P Inc.) | Vink, Jeroen C. (Shell International E&P Inc.) | Chen, Chaohui (Shell International E&P Inc.) | Weber, Daniel (Shell International E&P Inc.) | Alpak, Faruk O. (Shell International E&P Inc.) | Hoek, Paul van (Shell International E&P Inc.) | Pirmez, Carlos (Shell Nigeria Exploration and Production Company)
Abstract Quantitative integration of 4D seismic data with production data into reservoir models is a challenging task. One important issue is how to properly quantify the uncertainty, or the posterior probability distribution (PPD). The Very Fast Simulated Annealing (VFSA) is a stochastic searching method, whereas the Simultaneous Perturbation and Multivariate Interpolation (SPMI) is a model-based local searching method. The stochastic features of the VFSA provide the feasibility of identifying possible multiple peaks of a PPD, but it converges very slowly. On the other hand, the model-based SPMI method has the advantages of effectively utilizing the smooth features of an objective function, and thus can converge to local optimum very quickly. More importantly, the Hessian of the objective function, or the covariance matrix of the PPD, can be estimated by the SPMI method with satisfactory accuracy. However, it is very difficult to identify multiple optima by applying the SPMI method alone. In this paper, we propose an efficient joint inversion workflow by appropriately integrating the two derivative free optimization (DFO) methods. The complementary features of the two methods can further improve both applicability and efficiency of this joint inversion workflow. We tested the workflow with a 3D synthetic model and a real field case. Our results show that the integrated method is efficient and can deliver good results for jointly assimilating 4D seismic and production data.
- Europe (1.00)
- Africa (0.68)
- North America > United States > Texas (0.46)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- Europe > United Kingdom > Atlantic Margin > West of Shetland > Faroe-Shetland Basin > Judd Basin > Block 204/25 > Greater Schiehallion Field > Schiehallion Field (0.99)
- Europe > United Kingdom > Atlantic Margin > West of Shetland > Faroe-Shetland Basin > Judd Basin > Block 204/20 > Greater Schiehallion Field > Schiehallion Field (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 104 > Block 30/9 > Oseberg Field > Tarbert Formation (0.99)
- (6 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.96)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.88)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.70)
Assisted History Matching Using Three Derivative-Free Optimization Algorithms
Chen, Chaohui (Shell International E&P Inc.) | Jin, Long (Shell International E&P Inc.) | Gao, Guohua (Shell International E&P Inc.) | Weber, Daniel (Shell International E&P Inc.) | Vink, Jeroen C. (Shell International E&P Inc.) | Hohl, Detlef F. (Shell International E&P Inc.) | Alpak, Faruk O. (Shell International E&P Inc.) | Pirmez, Carlos (Shell Nigeria Exploration and Production Company)
Abstract Gradient-based optimization algorithms can be very efficient in history matching problems. Since many commercial reservoir simulators do not have an adjoint formulation built in, exploring capability and applicability of derivative-free optimization (DFO) algorithms is crucial. DFO algorithms treat the simulator as a black box and generate new searching points using objective function values only. DFO algorithms usually require more function evaluations, but this obstacle can be overcome by exploiting parallel computing. This paper tests three DFO algorithms, Very Fast Simulated Annealing (VFSA), Simultaneous Perturbation and Multivariate Interpolation (SPMI) and Quadratic Interpolation Model-based (QIM) algorithm. Both SPMI and QIM are model-based methods. The objective function is approximated by a quadratic model interpolating points evaluated in previous iterations, and new search points are obtained by minimizing the quadratic model within a trust region. VFSA is a stochastic search method. These algorithms were tested with two synthetic cases (IC fault model and Brugge model) and one deepwater field case. Principal Component Analysis is applied to the Brugge case to parameterize the reservoir model vector to less than 40 parameters. We obtained good matches with all three derivative-free methods. In terms of number of iterations used for converging and the final converged value of the objective function, SPMI outperforms the others. Since SPMI generates a large number of perturbation and search points simultaneously in one iteration, it requires more computer resources. QIM does not generate as many interpolation points as SPMI, and it converges more slowly in terms of time. VFSA is a sequential method and usually requires hundreds of iterations to converge.
- Europe (0.46)
- North America > United States (0.28)
- Geophysics > Seismic Surveying (0.68)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (0.46)