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Results
Grid-Based Surrogate Reservoir Modeling (SRM) for Fast Track Analysis of Numerical Reservoir Simulation Models at the Grid block Level
Mohaghegh, Shahab D. (West Virginia University & Intelligent Solutions, Inc.) | Amini, Shohreh (West Virginia University) | Gholami, Vida (West Virginia University) | Gaskari, Razi (Intelligent Solutions, Inc.) | Bromhal, Grant (U.S. Deaprtment of Energy, NETL)
Abstract Developing proxy models has a long history in our industry. Proxy models provide fast approximated solutions that substitute large numerical simulation models. They serve specific useful purposes such as assisted history matching and production/injection optimization. Most common proxy models are either reduced models or response surfaces. While the former accomplishes the run-time speed by grossly approximating the problem the latter accomplishes it by grossly approximating the solution space. Nevertheless, they are routinely developed and used in order to generate fast solutions to changes in the input space. Regardless of the type of model simplifications that is used, these conventional proxy models can only provide, at best, responses at the well locations, i.e. pressure or rate profiles at the well. In this paper we present application of a new approach to building proxy models. This method has one major difference with the traditional proxy models. It has the capability of replicating the results of the numerical simulation models, away from the wellbores. The method is called Grid-Based Surrogate Reservoir Model (SRM) since it is has the unique capability of being able to replicate the pressure and saturation distribution throughout the reservoir at the grid block level, and at each time step, with reasonable accuracy. Grid-Based SRM performs this task at high speed, when compared with conventional numerical simulators such as those currently in use (commercial and in-house) in our industry. To demonstrate the capabilities of Grid-Based SRM, its application to three reservoir simulation models are presented. Fist is a giant oil field in the Middle East with a large number of producers, second, to a CO2 sequestration project in Australia, and finally to a numerical simulation study of potential carbon storage site in the United States. The numerical reservoir simulation models are developed using two of the most commonly used commercial simulators1. Two of the models presented in this manuscript are consisted of hundreds of thousands of grid blocks and one includes close to a million cells. The Grid-based SRM that learns and replicates the fluid flow through these reservoirs can open new doors in reservoir modeling by providing the means for extended study of reservoir behavior with minimal computational cost. Surrogate Reservoir Modeling (SRM) is classified as an AI-Based reservoir model (Mohaghegh, 2011) referring to a process that accomplishes the task of proxy modeling by learning the specific behavior of a numerical reservoir simulation model through training on a uniquely developed spatio-temporal dataset. The spatio-temporal dataset is developed for each model using only a handful of simulation runs.
- Africa (0.89)
- Asia > Middle East > Saudi Arabia (0.28)
- North America > United States > Texas (0.28)
Application of Well-Based Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study
Mohaghegh, Shahab D. (West Virginia University & Intelligent Solutions, Inc.) | Liu, Jim (Saudi Aramco) | Gaskari, Razi (Intelligent Solution Inc.) | Maysami, Mohammad (Intelligent Solution Inc.) | Olukoko, Olugbenga (Saudi Aramco)
Abstract Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional proxy models, such as response surfaces or reduced models. These advantages include (1) to develop an SRM one does not need to approximate the existing simulation model, (2) the number of simulation runs required for the development of an SRM is at least an order of magnitude less than traditional proxy models, and (3) above and beyond representing the pressure and production profiles at each well individually, SRM can replicate, with high accuracy, the pressure and saturation changes at each grid block. Well-based SRM is based on the pattern recognition capabilities of artificial intelligence and data mining (AI&DM) that is also referred to as predictive analytics. During the development process the SRM is trained to learn the principles of fluid flow through porous media as applied to the complexities of the reservoir being modeled. The numerical reservoir simulation model is used for two purposes: (1) to teach the SRM the physics of fluid flow through porous media as applied to the specific reservoir that is being modeled, and (2) to teach the SRM the complexities of the heterogeneous reservoir represented by the geological model and its impact on the fluid production and pressure changes in the reservoir. Application of well-based SRM to two offshore fields in Saudi Arabia is demonstrated. The simulation model of these fields includes millions of grid blocks and tens of producing and injection wells. There are four producing layers in these assets that are contributing to production. In this paper we provide the details that is involved in development of the SRM and show the result of matching the production from the all the wells. We also present the validation of the SRM through matching the results of blind simulation runs. The steps in the development of the SRM includes design of the required simulation runs (usually less than 20 simulation runs are sufficient), identifying the key performance indicators that control the pressure and production in the model, identification of input parameters for the SRM, training and calibration of the SRM and finally validation of the SRM using blind simulation runs.
- North America > United States (1.00)
- Africa (0.94)
- Asia > Middle East > Saudi Arabia (0.86)
Abstract Water is injected in the hydrocarbon reservoir to serve two purposes, to maintain reservoir pressure and to displace oil as production proceeds in the reservoir. In recent years, smart wells coupled with reservoir simulation models are used to improve the results of water injection performance. High frequency data (pressure, flow rate, etc.) that is a product of the smart wells provide the basis for a closed-loop, fast track updating of the dynamic reservoir models. While high frequency updating of the reservoir model remains a challenge, there are emerging technologies that can make such objectives achievable. An integrated approach that combines analytical and numerical solutions with artificial intelligence and data mining is proposed to ultimately achieve the closed-loop, fast track updating system. This study is the first step in that direction. In this work the ability of analytical solutions to calculate reservoir water saturation profiles from field water cut data are investigated. Different flow regimes and reservoir geometries are considered during this study. Diffuse, segregated and capillary influenced flow models are analyzed in both one and two dimensional water injection using a commercial numerical simulator. Different analytical formulations are applied for each flow regime in order to reproduce simulation production data. For each model a specific relative permeability relation is assigned and tuned with the aim of matching water breakthrough time and water cut history. An accurate match is achieved between water saturation profiles generated by the analytical models and the results by the reservoir simulator. The influence of simple reservoir heterogeneity on the robustness of the analytical models is studied.
- North America > United States (0.68)
- Africa (0.46)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.56)
- North America > United States > Arkansas > Smart Field (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Rokan Block > Bekasap Field > Menggala Formation (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Rokan Block > Bekasap Field > Bekasap Formation (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Rokan Block > Bekasap Field > Bangko Formation (0.99)
Application of Surrogate Reservoir Model (SRM) to an Onshore Green Field in Saudi Arabia; Case Study
Mohaghegh, Shahab D. (Intelligent Solutions, Inc. & West Virginia University) | Liu, Jim (Saudi Aramco) | Gaskari, Razi (Intelligent Solutions, Inc.) | Maysami, Mohammad (Intelligent Solutions, Inc.) | Olukoko, Olugbenga A. (Saudi Aramco)
Abstract Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling. In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells. Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run. SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.55)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Mining (0.88)