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Radio Frequency Heating Simulation Using A Reservoir Simulator Coupled with Electromagnetic Solver for Soil Remediation
Guan, Xiaoyue (Chevron) | Li, Gary (Chevron) | Wang, Hanming (Chevron) | Shang, Shubo (Chevron) | Tokar, Timothy (Chevron) | McVey, Kevin (Chevron) | Ovalles, Cesar (Chevron) | Wu, Dagang (University of Houston) | Chen, Ji (University of Houston)
Abstract Radio frequency (RF) heating is recognized as a technique having the potential to thermally enhance remediation of hydrocarbon-impacted soil. RF heating delivers electromagnetic (EM) power to a targeted body of soil, resulting in an increased soil temperature that enhances the in-situ remediation processes such as biodegradation. Antennas are placed either on the ground or installed in the soil near the ground surface. The antennas operate in the hundreds of kHz to MHz range. To model the RF heating process, we successfully coupled a reservoir simulator with a 3-dimensional (3D) EM solver to evaluate the ability of RF technology to heat soil in situ. The coupled reservoir/EM simulator solves the EM fields and associated heating for a heterogeneous reservoir or soil volume in the presence of multiple antennas. The coupling was accomplished through a flexible interface in the reservoir simulator that allows the runtime loading of third-party software libraries with additional physics. This coupled workflow had been previously used for studying RF heating for heavy oil recovery (Li 2019). An RF heating simulation case study was performed in support of a soil remediation field test designed to demonstrate the ability to heat soils using EM energy. The study included field test data analysis, simulation model building, and history matching the model to test data. Results indicate, on average, the soil was heated โผ2-3ยฐC above the initial formation temperature after approximately two days (52 hours) of RF heating. We found that the RF heating was local, and our simulation model, after tuning input parameters, was able to predict a temperature profile consistent with the field test observations. With properly designed RF heating field pilots and tuning of EM and reservoir parameters in simulation models, the coupled reservoir/EM simulator is a powerful tool for the calibration, evaluation, and optimization of RF heating operations.
- North America > United States (1.00)
- Europe > United Kingdom > North Sea > Central North Sea (0.24)
- Africa > South Africa > Western Cape Province > Indian Ocean (0.24)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Software (0.68)
Integrated Modeling for Assisted History Matching and Robust Optimisation in Mature Reservoirs
Nguyen, Ngoc T. (University of Calgary) | Chen, Zhangxin (University of Calgary) | Dang, Cuong T. (Computer Modelling Group Ltd.) | Nghiem, Long X. (Computer Modelling Group Ltd.) | Yang, Chaodong (Computer Modelling Group Ltd.) | Bourgoult, Gilles (Computer Modelling Group Ltd.) | Li, Heng (Computer Modelling Group Ltd.)
Abstract History matching and production optimization are the important keys in reservoir modeling. Reservoir geology plays a crucial role in these complicated processes and the oil and gas operators highly demand for a fast, accurate and effective integration of geology and reservoir engineering that is still limited in the past. This paper aims to present: (1) a modeling approach that automatically integrates geological modeling software, a reservoir simulator, and history matching and optimization software in a closed-loop; (2) the advantages of an assisted history matching approach; (3) a robust optimization workflow based on multiple geological realizations. This new approach was successfully applied in a Brugge reservoir. We first present a systematical workflow of the integrated modeling that allows us to capture the crucial effects of geology. Based on this approach, multiple geological realizations are geostatiscally generated for history matching, robust optimization and uncertainty assessment. We utilize the benefits of coupling the geological modeling software, the reservoir simulator and the optimization tool together. The Designed Exploration and Controlled Evolution optimization method is used to perform the history matching. In the history matching process, the optimizer invokes geological software with new variogram parameters to calculate the reservoir properties. Finally, a robust optimization approach based on multiple geological realizations is introduced to overcome the current weakness of optimization based on single realization. The closed loop modeling approach is proven a powerful tool to improve the modeling quality and reduce the time and engineering efforts for capturing the critical effects of reservoir geology in complex sandstone reservoirs. Using this closed loop modeling, we successfully perform an assisted history matching of the secondary waterflooding process. This method effectively accounts for the uncertainty of geological characteristics in terms of facies proportion and spatial distribution. The properties of each facies were controlled by both blocked data histograms and the vertical trend. The results of history matching show that the misfit of objective functions was reduced from 16.45% to 6%. Finally, we optimize the rates of thirty injection and production wells over the life of a reservoir, with the objective to maximize the average NPV based on the geological realizations that the history misfit is less than 7% rather than using a single realization. The results show that the robust optimization significantly improve the expected NPV and reduce substantially the risk associated with geological uncertainties. This paper presents an efficient modeling and optimization approach under geological uncertainties by integrating various simulator's available in the industry. The innovative closed loop modeling workflow together with an assisted history matching and robust optimization provides a means of optimizing recovery and assessing uncertainties of both secondary and tertiary EOR processes.
Proposal The BP Trinidad and Tobago (bpTT) portfolio of assets consists of six producing gas assets, three mature oil fields and several undeveloped gas fields. In addition to significant efforts to meet a growing gas demand for Trinidad and Tobago's LNG and domestic markets, the focus is currently on optimization of oil production. Understanding the uncertainties and optimizing depletion plans is key to successful reservoir management in gas and oil fields. BpTT uses reservoir simulation to history match and then predict future reservoir performance from highly faulted, stacked sands in Trinidad. Traditionally, a reservoir engineer spends a significant portion of the project time in the construction and history match of the reservoir model to gain confidence for performance prediction. Due to time and resource constraints this generally means that only one model is history-matched. A handful of single parametric uncertainties might be investigated, but decisions are frequently based on the one model thought to be most likely. BpTT began applying BP's Top-Down Reservoir Modeling (TDRM) philosophy [Williams, et al] in October 2003. TDRM uses a genetic algorithm to assist the engineer in building multiple history matches of the dynamic data based on a user-defined precision, ranges and probability distributions for key input uncertainties. This workflow incorporates a variety of reservoir uncertainties in multiple, probable reservoir models, all of which match the production history within specified precision. Future field and well performance is then optimized by evaluating a range of potential outcomes, rather than by prediction from one single description, as often seen in a traditional manual modeling approach. This paper relates the successful use of TDRM in bpTT for optimization of infill well locations in a mature oil field. It includes the history match of 30-years of production from 13 wells in the Teak field (Figure 1). The TDRM cycle including history match and prediction was completed in three weeks. The study used multiple scenarios with 16 history-matched models to broaden the scope of analysis of the reservoirs in a time efficient manner. Introduction TDRM is BP's proprietary technology that is changing the manner in which reservoir engineers approach, perform and interpret performance prediction. TDRM fully encapsulates the reservoir uncertainties in the simplest appropriate model that answers the pertinent question that needs to be answered; only adding the level of detail required (Figure 2). With sparse datasets and the need to optimize reservoirs, these appropriate models are the best tools for understanding the sensitivity of the performance prediction.
- North America > Trinidad and Tobago (0.75)
- North America > United States (0.49)
- North America > Trinidad and Tobago > Trinidad > North Atlantic Ocean > Columbus Basin > TSP Block > Teak Field > Moruga Formation > Gros Morne Formation (0.99)
- South America > Ecuador > Oriente Basin > Napo Formation > Napo T Formation (0.98)