Reservoir Management Optimization Using Permanent Downhole Gauge Data

Silva, Marcia Ida de Oliveira; Kato, Edson Tsuneo

doi:10.2118/90973-ms

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…Laurence and Brown, 2000;Tolan et al, 2001;Manin, 2002;Brown et al, 2003Brown et al, , 2004Bui and Jalali, 2004;Kragas et al, , 2004Silva and Kato, 2004;Webster et al, 2004). These data help reduce uncertainty in the reservoir description, and contribute to reservoir management decisions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Measurements of Streaming Potential for Downhole Monitoring in Intelligent Wells

Jaafar

Jackson

Saunders

et al. 2009

SPE Middle East Oil and Gas Show and Conference

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Downhole monitoring of streaming potential, using electrodes mounted on the outside of insulated casing, is a promising new technology for monitoring water encroachment towards an intelligent well. However, there are still significant uncertainties associated with the interpretation of the measurements, particularly concerning the streaming potential coupling coefficient. This is a key petrophysical property which dictates the magnitude of the streaming potential for a given fluid potential. The coupling coefficient can be measured experimentally, but previous studies have obtained data for sandstone cores saturated with relatively low salinity brine (less than seawater). Formation and injected brine in hydrocarbon reservoirs is typically more saline than this. Extrapolating data obtained at low salinity into the high salinity domain suggests that the coupling coefficient falls to zero at approximately seawater salinity. If this is the case, then streaming potential signals will be very small in most hydrocarbon reservoirs.We present the first measured values of streaming potential coupling coefficient in sandstone cores saturated with brine at higher than seawater salinity. We find that the coupling coefficient is small, but still measureable, even when the brine salinity approaches the saturated concentration limit. Consistent results are obtained from two independent experimental setups, using specially designed electrodes and paired pumping experiments to eliminate spurious electrical potentials. We apply the new experimental data in a numerical model to predict the streaming potential signal which would be measured at a well during production. The results suggest that measured signals should be resolvable above background noise in most hydrocarbon reservoirs, and that water encroaching on a well could be monitored while it is several tens to hundreds of metres away.

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Section: Introductionmentioning

confidence: 99%

Measurements of Streaming Potential for Downhole Monitoring in Intelligent Wells

Jaafar

Jackson

Saunders

et al. 2009

SPE Middle East Oil and Gas Show and Conference

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“…de Oliveira and Kato (2004) showed a real field example in Campos Basin, Brazil, which demonstrated a full workflow of integrating PDG data into reservoir management optimization. The work started from using the PDG data in reservoir characterisation, to reservoir development, to the production optimization.…”

Section: Reservoir Monitoring and Managementmentioning

confidence: 99%

“…This provides a direction of the improvement of the history matching models. Figure 2.2: History matching using PDG data from de Oliveira and Kato (2004). Kragas et al (2004) presented a list of applications utilizing the PDG data in reservoir monitoring and management.…”

Section: Reservoir Monitoring and Managementmentioning

confidence: 99%

Interpreting Pressure and Flow Rate Data from Permanent Downhole Gauges Using Data Mining Approaches

Liu

Horne

2011

SPE Annual Technical Conference and Exhibition

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The Permanent Downhole Gauge (PDG) is a promising tool for reservoir testing, but has yet to reach its full potential. Generally, conventional well testing methods are most able to utilize small sections of constant flow rate data. However, data mining, a newly developed technique in computer science, is a tool that can reveal the relationship among variables from large volumes of data. The application of data mining algorithms to synthetic and field data has been successful in extracting the reservoir model from variable flow rate and pressure transient data.The application is conducted in two steps. As a first step, the pressure and flow rate data from the PDG are used to train a nonparametric data mining algorithm. The reservoir model is obtained implicitly in the form of polynomials in a highdimensional Hilbert space when the algorithm converges after being trained to the data. In the second step, a constant flow rate input is fed into the data mining algorithm. The data mining algorithm will make a pressure prediction subject to the input flow rate. As the data mining algorithm has already obtained the reservoir model, the pressure prediction is expected to be the reservoir response given the constant flow rate. Therefore, the proposed constant flow rate and the predicted pressure are the reservoir model underlying the variable PDG data.Noisy synthetic data and the real field data were used to test this approach. The method was able to recover the reservoir model successfully. Even at the extreme cases when the flow rate data are very noisy and changing frequently, and in the absence of any shut-ins, the method was still able to extract the reservoir models.

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“…In addition, history matching could be added as described by several authors, for example de Oliveira and Kato (2004).…”

Section: Reservoir-engineering Uses Of Pdg Datamentioning

confidence: 99%

Listening to the Reservoir—Interpreting Data From Permanent Downhole Gauges

Horne

2007

Journal of Petroleum Technology

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The permanent downhole pressure gauge is a class of tool that has become popular in the industry. These tools are installed during the well completion and provide a continuous record of pressure changes during production. Permanent downhole gauges (PDGs) have potential to provide more information than is available with traditional well testing, which is carried out for a relatively short duration. PDGs may provide useful information regarding changes in reservoir properties or well conditions with time as the reservoir is produced.However, interpretation of PDG data is a new problem. Unlike the traditional well test in which "disturbances" in the reservoir (i.e., rate changes) are created and pressure and rates are both known, the changes in rates associated with the record from the PDG may not be known. Moreover, the dynamic changes in the reservoir, along with changes in the flowing temperature or changes in the gauge itself, make the data more complicated to interpret.

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Reservoir Management Optimization Using Permanent Downhole Gauge Data

Cited by 12 publications

References 13 publications

Measurements of Streaming Potential for Downhole Monitoring in Intelligent Wells

Measurements of Streaming Potential for Downhole Monitoring in Intelligent Wells

Interpreting Pressure and Flow Rate Data from Permanent Downhole Gauges Using Data Mining Approaches

Listening to the Reservoir—Interpreting Data From Permanent Downhole Gauges

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