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Collaborating Authors
Energy
Ocean Bottom Node Seismic: Learnings From Bonga, Deepwater Offshore Nigeria
Detomo, Rocco (Shell Nigeria Exploration and Production Company) | Quadt, Edwin (Shell Nigeria Exploration and Production Company) | Pirmez, Carlos (Shell Nigeria Exploration and Production Company) | Mbah, Reginald (Shell Nigeria Exploration and Production Company) | Olotu, Samuel (Shell Nigeria Exploration and Production Company)
Summary The Bonga Main field was discovered by Shell Nigeria Exploration and Production Company (SNEPCO) in 1995 and is located some 120 km offshore Nigeria at water depths of 800m - 1200m. First oil production started in November 2005. Bonga is Nigeria's first deepwater discovery. Oil production is from the main stacked reservoirs (690, 702, 710/740 and 803) through several producer/injector well pairs. The Bonga Main field is covered by a block-wide exploration 3D seismic streamer survey conducted in 2000 which serves as the baseline 4D seismic survey. As part of the Fieldโs Life-Cycle Development Plan, 4D Time Lapse serves as an integral part of the monitoring of the producing reservoirs. Dynamic reservoir modelling predicted that the Bonga reservoirs would be conducive to generating robust time-lapse signals of the water injection sweep and associated fluid movements, with pressure effects expected to be secondary due to the fieldโs pressure maintenance program. Subsequently, a very successful 4D time-lapse seismic streamer monitor survey was acquired over the Bonga Main field in 2008, with the objective of providing early confirmation of fluid movements and reservoir models, for optimum management of the producing reservoirs. Although extremely successful, it became clear that streamer seismic would not be the best future option for imaging the up-dip reservoirs under the FPSO. Subsequently, an Ocean Bottom Node Survey was proposed and acquired in 2010 to serve as an up-dip seismic baseline for future OBN monitors seismic surveys. This paper will give a brief overview of the Bonga 4D streamer seismic results, outline the case for action for acquiring an OBN seismic survey, discuss the design criteria, acquisition and processing of this baseline OBN, and conclude with a comparisons of the OBN to the streamer data, highlighting the associated business implications.
- Africa > Nigeria > Gulf of Guinea > Niger Delta > Niger Delta Basin > OPL 217 > Agbami-Ekoli Field > Agbami Field (0.99)
- Africa > Nigeria > Gulf of Guinea > Niger Delta > Niger Delta Basin > OPL 216 > Agbami-Ekoli Field > Agbami Field (0.99)
- Africa > Nigeria > Gulf of Guinea > Niger Delta > Niger Delta Basin > OML 118 > Bonga Field (0.99)
Abstract Quantitative integration of spatial and temporal information provided by time-lapse (4D) seismic surveys to dynamic reservoir models calls for an efficient and effective workflow. To solve this issue, we propose a novel workflow which uses a Bayesian/MCMC approach and experimental design-based proxies for selected 4D seismic observables to update dynamic reservoir models. This methodology includes the following steps: (1) create probability maps to select locations where 4D seismic data is assimilated; (2) run a sensitivity analysis; (3) create high-order proxy models; and (4) run an MCMC inversion to determine a set of models that best fit the 4D seismic data and quantify uncertainty. This new workflow has been applied in 3 cases including two synthetic models and one field case. This first synthetic example is called the Imperial College Fault Model (ICFM).The second synthetic model is a fluvial reservoir model with 10 uncertain parameters. The field example is a deepwater turbidite reservoir undergoing a waterflood with a reasonably long production history and high-quality 4D seismic data. Following the four steps of this workflow, all the models are successfully history matched by conditioning to 4D seismic data. Uncertainty quantification was also provided as part of the MCMC inversion. We also compare different scenarios using production data and/or 4D seismic data in the model updating process to show the value of the 4D seismic data. For our field case, the updated models can be used for production forecasting, reserves booking and identification of further development opportunities.
- Europe (1.00)
- North America > United States > Texas (0.29)
- 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)
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)