A Step Ahead Of 4D: The Integration Of Basic And Analytical Techniques To Drive The Changes In The Reservoir Simulation Model History Match Case Study

2014 ◽  
Author(s):  
Hector Gomez Alonso ◽  
Runer Avila ◽  
Bret Peppard ◽  
Vicki Gee ◽  
Joao Baima ◽  
...  
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
Analytical Techniques ◽  
History Match ◽  
Reservoir Simulation Model

Robust Life-Cycle Production Optimization With a Support-Vector-Regression Proxy

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2409-2427 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Simulation Model ◽  
Support Vector Regression ◽  
Reservoir Simulation ◽  
Production Optimization ◽  
Forward Model ◽  
Support Vector ◽  
Well Control ◽  
Steepest Ascent ◽  
Reservoir Simulation Model

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

A Proxy Model for Predicting SAGD Production From Reservoirs Containing Shale Barriers

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Jingwen Zheng ◽  
Juliana Y. Leung ◽  
Ronald P. Sawatzky ◽  
Jose M. Alvarez
Keyword(s):  
Simulation Model ◽  
Network Model ◽  
Reservoir Simulation ◽  
Oil Sands ◽  
Training Set ◽  
Steam Assisted Gravity Drainage ◽  
Proxy Model ◽  
Reservoir Simulation Model ◽  
The Individual ◽  
Operational Constraints

Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.

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