Reservoir Aspects of Smart Wells

Glandt, C.A.

doi:10.2118/81107-ms

Cited by 20 publications

(5 citation statements)

References 40 publications

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“…The main characteristic of smart well technology is to actively intervene in the hydrocarbon production process by means of downhole flow sensing and control [1]. Although downhole measurements are quite common in the form of pressure gauges or (distributed) temperature sensors (DTS), flow rates are rarely measured directly.…”

Section: Introductionmentioning

confidence: 99%

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Gryzlov

Schiferli

Mudde

2013

Flow Measurement and Instrumentation

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Section: Introductionmentioning

confidence: 99%

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Gryzlov

Schiferli

Mudde

2013

Flow Measurement and Instrumentation

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“…Various methods have been suggested to mitigate this problem. Among these is smart well completions where the production or the injection section is divided into several intervals (Arenas and Dolle 2003;Glandt 2003;Hussain et al 2005). The flow rate at each interval can be independently controlled by inflow control valves (ICVs); hence, making it possible to control flow rates across the high permeability streaks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Alhuthali

2009

SPE Annual Technical Conference and Exhibition

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Waterflood optimization via rate control is receiving increased interest because of rapid developments in the smart well completions and I-field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. It is well recognized that field scale rate optimization problems are difficult because they often involve highly complex reservoir models, production and facilities related constraints and a large number of unknowns. Some aspects of the optimization problem have been studied before using mainly optimal control theory.However, the applications to-date have been limited to rather small problems because of the computation time and the complexities associated with the formulation and solution of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep efficiency under realistic field conditions has still remained largely unexplored.We propose a practical and efficient approach for computing optimal injection and production rates and thereby manage the waterflood front to maximize sweep efficiency and delay the arrival time to minimize water cycling. Our work relies on equalizing the arrival times of the waterfront at all producers within selected sub-regions of a water flood project. The arrival time optimization has favorable quasi-linear properties and the optimization proceeds smoothly even if our initial conditions are far from the solution. We account for geologic uncertainty using two optimization schemes.The first one is to formulate the objective function in a stochastic form which relies on a combination of expected value and standard deviation combined with a risk attitude iv coefficient. The second one is to minimize the worst case scenario using a min-max problem formulation. The optimization is performed under operational and facility constraints using a sequential quadratic programming approach. A major advantage of our approach is the analytical computation of the gradient and Hessian of the objective which makes it computationally efficient and suitable for large field cases.Multiple examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include several 2D synthetic examples for validation purposes and 3D field applications.

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“…Fonte: Glandt, 2003 Os PCIs possuem a flexibilidade de controlar cada zona produtora de forma independente, o que permite uma rápida ação caso ocorra algum evento inesperado durante o desenvolvimento da produção de um campo de petróleo (Durlofky e Aziz,…”

Section: Figura 1: Representação Do Funcionamento Do Smart Wellunclassified

“…Toda a tecnologia empregada nos PCI -incluindo técnicas, softwares e equipamentos para a leitura de novos dados e informações sísmicasfornecem, na operação de produção de petróleo e gás, um maior controle de forma mais otimizada e precisa. Além disso, também é possível identificar novas áreas com potencial de exploração (Glandt, 2003).…”

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Aplicação De Técnicas De Machine Learning E Data Driven Em Poços Inteligentes De Petróleo

CALVETTE¹

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PUC-Rio -Certificação Digital Nº 1712515/CA quanto nos testes que utilizaram dados de produção observados (caso real), proporcionando uma variação de até 75% no RMSE e 85% no MAE. O erro RMSE normalizado na rede GRUB foi de 0,53% nos dados observados e 0,65% nos dados sintéticos. Os modelos de deep smart proxy obtiveram desempenhos muito semelhantes, principalmente ao comparar o desempenho das redes do tipo LSTMB e GRUB. Estas redes foram aplicadas em ambos os casos sintético e real de produção e superaram, em todos os casos, os resultados obtidos com o modelo de smart proxy com MLP. Palavras-chaveMachine learning; proxy; smart proxy; deep smart proxy; poços inteligentes; produção de petróleo.A reliable forecast for oil production represents one of the biggest challenges in the oil and gas industry and contributes to the planning and decision making of oil companies. Because of that, this work uses intelligent well valves settings and data driven methodology to explore the advantages and the performance of machine learning algorithms in the forecasting of oil, gas and water production. In order to do so, two database containing historical data series of oil, gas and water production were used. The first was generated synthetically (through reservoir simulation) and consisted of the average monthly production of an injection well over a period of 10 years, as well as the configuration of 3 of its valves. The second database used the production data provided by the state of South Dakota, located in the United States, and consisted of the daily production average and the overall well status (active or not producing) from several oil producing wells in a period ranging from 1950 to 2018. In order to test the methodology, several experiments were performed combining proxy with Artificial Neural Network Algorithms (Multilayer Perceptron) and deep learning recurrent neural networks (Simple Recurrent Neural Networks, long short-term memory, Gated Recurrent Units), which were named smart proxy and deep smart proxy, respectively. The results showed that the deep smart proxy model was very promising. Using the Gated Recurrent Units network with bi-directional layers (GRUB), a reduction of 66% in the RMSE error and 79% in the MAE error was obtained when compared to smart proxy models with Artificial Neural Networks.The deep smart proxy models with bidirectional layers generated a significant improvement in prediction and error reduction in both databases tests ( i.e. tests with simulated production data (synthetic case) and with the observed production data (real case), resulting in a variation of up to 75% in RMSE and 85% in MAE).The normalized RMSE error in the GRUB network was of 0.53% in the observed PUC-Rio -Certificação Digital Nº 1712515/CA database and 0.65% in the synthetic database. It is important to notice that the Deep smart proxy models achieved very similar performances when comparing the LSTMB and GRUB network in both databases (synthetic and real production), surpassing in all cases the results obtained with...

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Reservoir Aspects of Smart Wells

Cited by 20 publications

References 40 publications

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Aplicação De Técnicas De Machine Learning E Data Driven Em Poços Inteligentes De Petróleo

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