Insurance Value of Intelligent Well Technology against Reservoir Uncertainty

Addiego-Guevara, Ernesto; Jackson, Matthew D.; Giddins, Marie Ann

doi:10.2118/113918-ms

Cited by 45 publications

(27 citation statements)

References 56 publications

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“…We investigate the predicted streamingpotential measurements resulting from production from a single horizontal well with multiple completions. This is a first step toward developing a feedback control strategy (Addiego-Guevara et al, 2008), which may be used to enhance production.…”

Section: Introductionmentioning

confidence: 99%

Streaming potentials at hydrocarbon reservoir conditions

et al. 2012

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We have examined the behavior of the streaming potential under multiphase conditions, and under conditions of varying temperature and salinity, to evaluate the feasibility of using downhole streaming-potential measurements to determine fluid distributions in a reservoir. Using new insights into the pore-scale distribution of fluids and of electric charge, we found that the saturation dependence of the streaming potential coupling coefficient is important in determining the resulting streaming potential. Through examination of the four independent physical parameters which comprise the coupling coefficient, we developed an understanding of the behavior of the coupling coefficient under conditions of elevated temperature and brine salinity. We found that although increasing salinity substantially reduces the magnitude of the coupling coefficient, and therefore also the magnitude of the predicted streaming potential, increasing temperature has only a small effect, showing about a 10% change between 25°C and 75°C, depending on salinity.

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Section: Introductionmentioning

confidence: 99%

Streaming potentials at hydrocarbon reservoir conditions

et al. 2012

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“…This would result in an increase of the efficiency of the control strategies and a reduction of the uncertainties of the dynamic reservoir models (Addiego-Guevara et al, 2008;Jansen et al, 2008). The typical range of detection in the order of 10 m is particularly suitable for bottom water-drive reservoirs with thin oil layer-or steam-assisted gravity drainage processes.…”

Section: Resultsmentioning

confidence: 99%

“…The source of inspiration was a conceptual model analyzed by Addiego-Guevara et al (2008): a thin sandstone reservoir containing interbedded, laterally discontinuous shale barriers that reduce the effective vertical permeability. The porosity and permeability distributions, shown in Figure 1a and b, were created using an in-house geostatistical modeling software based on principal component analysis; a large number of realizations of typical geological features were used to create the final image (Sarma et al, 2008).…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The key parameters to run the reservoir and EM simulations are presented in Table 1. The rock and fluid properties for the flow simulations are chosen in agreement with values of similar reservoir models (Jansen et al, 2002;Addiego-Guevara et al, 2008); we refer to Miorali et al (2011) for an analysis of the EM properties at reservoir conditions and their effect on radar wave propagation. Archie's parameters are representative of a sandstone reservoir (Archie, 1942).…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Coupling ground penetrating radar and fluid flow modeling for oilfield monitoring applications

et al. 2011

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The recent introduction of smart well technology allows for new geophysical monitoring opportunities. Smart wells, which allow zonal production control, combined with monitoring techniques capable of capturing the arrival of undesired fluids, have the potential to significantly increase the oil recovery. We consider borehole radar as a valuable technology for monitoring of the near-well region. By coupling a drainage process of a bottom water-drive reservoir with electromagnetic simulations, we find that radar sensors located in the production well can successfully map the fluid saturation evolution. In low-conductivity reservoirs r < 0:02 S=m ð Þ , a system performance above 80 dB is necessary to record reflections in the range of 10 m.Higher conductivity values strongly reduce the radar investigation depth. Despite the technical challenges to implement a permanent down-hole radar system, the potential semi-continuous acquisition would make 4D groundpenetrating radar a promising technology in capturing the near-well fluid dynamics. Suitable environments are bottom water-drive reservoirs with thin oil layer and heavy oil reservoirs exploited by steam-assisted gravity drainage processes.

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“…They have combined geological and or economic uncertainty with different smart well completion configurations and several ICV control optimization strategies [van Essen et al 2010] [Grebenkin 2013] [Addiego-Guevara et al 2008] [Schiozer and da Silva 2009] [Sampaio et al 2009]. Although it has been recognized that the choice of an optimal well trajectory is crucial to maximize the economic value of a reservoir, no studies have been found on well trajectory optimization for smart wells.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Smart Completions on Optimal Well Trajectories

Maas

Bouts

Joosten

2017

Day 4 Thu, November 16, 2017

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-In the planning of new wells, typically the same trajectory is used for conventional wells and wells with smart completions. This study demonstrates that the economically optimized trajectory for smart and conventional wells can be very different. Two new well trajectory optimization algorithms were developed using Stochastic Pattern Search (SPS) principles. In both algorithms random perturbations are made starting from an initial well trajectory, which are sent to a reservoir simulator whereafter the perturbation with the highest Net Present Value (NPV) is selected. New perturbations of the selected well trajectory are made and simulated to, again, select the highest NPV. This process is repeated until a certain stopping criteria is met. The two methods differ in the selection of the perturbations used to initiate the new iteration. In the SPS1 method every subsequent iteration starts from the perturbation with the highest NPV which may be the starting well from the previous iteration. In the SPS2 method the starting well from the previous iteration is excluded. This does not allow the SPS2 method to converge, but it avoids one of the main risks of the SPS1 method, i.e. that the optimization remains stuck in a local optimum. To demonstrate the difference between the optimal well trajectory of well with a conventional and smart completion, both the SPS1 and SPS2 method were evaluated using a realistic, but slightly simplified reservoir model. Both methods were able to optimize the trajectory for both conventional and smart completions. The SPS1 method quickly converged to a local optimum, whilst the SPS2 method was able to determine a trajectory with a significantly higher NPV for both the conventional and smart wells. Moreover, the optimal well trajectory with the smart completion, as found by the SPS2 algorithm, had a NPV that was 40% higher than the optimal trajectory for the conventional completion. It can therefore be concluded that when smart completions are assessed, well trajectory optimization can have very significant value impact and may be crucial in evaluating the full potential of the completion. Furthermore it was shown that, for the example considered, the SPS2 procedure is a good method for well trajectory optimization in a three-dimensional reservoir and although more testing is needed it is believed that is has potential to work with any type of completion.

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Insurance Value of Intelligent Well Technology against Reservoir Uncertainty

Cited by 45 publications

References 56 publications

Streaming potentials at hydrocarbon reservoir conditions

Streaming potentials at hydrocarbon reservoir conditions

Coupling ground penetrating radar and fluid flow modeling for oilfield monitoring applications

The Impact of Smart Completions on Optimal Well Trajectories

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