A Literature Review on Smart-Well Technology

Chang, Guiran; Rajeswaran, Raj; Nakagawa, Edson

doi:10.2118/106011-ms

Cited by 40 publications

(10 citation statements)

References 12 publications

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“…The first smart completion was installed in August 1997 in the North Sea, Norway (Changhong, G, 2007). The principle of smart well completion is to optimize the operation of the well by closing and opening of the perforations and, hence, to maximize its production.…”

Section: Smart Completionmentioning

confidence: 99%

Smart Completion Design for Managing Steam Injection in CSS Process

et al. 2014

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In conventional cyclic steam stimulation (CSS), steam is periodically injected at high pressure to the reservoir. After the steam injection period, the well is soaked for several days then it will be produced. Generally, for the first to the fifth cycle, the steam can effectively transfer the heat to the reservoir. After that, the CSOR will rise up indicating that the process is currently ineffective. This paper aims to improve the CSS performance using modified well completion. The perforation is modified to become two parts, one part is on the top side (as injection) and the other part is on the bottom side (as production). In this process, after being injected, the steam will condense, resulting from heat loss, and it will move to lower part because of gravity drainage. Simultaneously, crude oil was produced through the production perforation. The opening-closing of the injectionproduction cycle is managed by interval control valve (ICV). To provide an overview of this phenomenon, a synthetic reservoir model was built based on Pertama-kedua formation, located in Sumatra Indonesia. Sensitive variables are length of the injection-production perforation and soaking time. Finally, the heat efficiency was evaluated during 8 years of project life. Simulation results show that dividing the perforation into injection and production intervals will reduce CSOR 30% and this requires shorter soaking time compared to that of conventional processes. Furthermore, if the distance between injection and production interval is longer the production will be better. However, this gap is limited by reservoir thickness.

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Section: Smart Completionmentioning

confidence: 99%

Smart Completion Design for Managing Steam Injection in CSS Process

et al. 2014

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“…This technique is relatively expensive to implement, as cables and related facilities cost a lot. Meanwhile, there are problems for the maintenance and connection of the cables at the joint between pipes in the string (Gao, Rajeswaran, & Nakagawa, 2007). The cables may be destroyed when they have been buried underground for a long time.…”

Section: Introductionmentioning

confidence: 99%

Research on pulse signal identification algorithms based on adaptive filter and adaptive generalized cross-correlation

Yang

Hua

Kan

et al. 2018

Systems Science & Control Engineering

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In the area of water injection, pulse telemetry is a new and effective communication technology which is critical for the intelligent oilfield. Under a complex environment with unknown noises, numerous problems need to be solved in the process of pulses identifying and data decoding, and there are difficulties in researching the related algorithms. In this paper, the characteristics of the pulse signals and noises are analysed respectively, and an adaptive filter algorithm is applied to reduce the impact of noises in pulse signal processing. Then a new adaptive generalized crosscorrelation algorithm is proposed to extract pulses by using a new weighted function, which can restrain interferences and noises in the frequency domain. By means of these algorithms, the pulse signal is more obvious with effective noise restraint, so these algorithms are more appropriate in water injection. Final results of field test indicate the algorithms have a high recognition performance and they meet the requirements of engineering application.

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“…Since the first smart well was installed at Saga's Snorre Tension Leg Platform in North Sea in 1997, more than 300 smart well systems have been installed worldwide (Gao, Rajeswaran, and Nakagawa 2007). The benefits of smart wells have been demonstrated in theoretical studies and practical applications (Brouwer et al 2001;Jansen et al 2002;Ramakrishnan 2007;van Essen et al 2009;Meshioye et al 2010;van Essen et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Using Multiscale Regularization to Obtain Realistic Optimal Control Strategies

Shuai

White

Zhang

et al. 2011

SPE Reservoir Simulation Symposium

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Smart well technology is attracting increasing attention because it promises to add operation flexibility and potentially increases oil recovery. However, it remains difficult to find the best strategy for production optimization. One challenge is to find a good optimization algorithm, as some optimization algorithms require prohibitive work to compute the gradients of the objective function with respect to the well controls. These methods also require access to the simulator code, which makes them difficult to use with commercial software. Another challenge is to find a reasonable frequency for well control adjustment: adjusting well controls too frequently imposes unrealistic control burdens on operations, increasing well management cost. Moreover, high-frequency control adjustment increases the risk of optimization algorithms being trapped at local optima as the problem is more under-determined. On the other hand, excessively low-frequency control adjustment may not truly optimize oil recovery. To address these issues, two simulator-independent optimization algorithms were investigated: ensemble-based optimization (EnOpt) and bound optimization by quadratic approximation (BOBYQA). Multiscale regularization was applied to both to find appropriate frequencies for well control adjustment. In a synthetic case study, if multiscale regularization was not used, then EnOpt converged to a higher net value of production than BOBYQA-even though BOBYQA uses second order Hessian information (EnOpt uses first order gradients). BOBYQA performed comparably only if multiscale regularization was used. After multiscale regularization, both methods obtained net value of production (NVP) that equalled or exceeded unregularized optimization, with simpler well control strategies and convergence in fewer iterations.

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A Literature Review on Smart-Well Technology

Cited by 40 publications

References 12 publications

Smart Completion Design for Managing Steam Injection in CSS Process

Smart Completion Design for Managing Steam Injection in CSS Process

Research on pulse signal identification algorithms based on adaptive filter and adaptive generalized cross-correlation

Using Multiscale Regularization to Obtain Realistic Optimal Control Strategies

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