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# Dynamic data

Dynamic data is information that changes asynchronously as the information is updated. Unlike static data, which is infrequently accessed and unlikely to be modified, or streaming data, which has a constant flow of information, dynamic data involves updates that may come at any time, with sporadic periods of inactivity in between.[1]

## Dynamic data in reservoir engineering

In the context of reservoir engineering, dynamic data is used during the creation of a reservoir model in conjunction with historical static data. [2] A reservoir model based on both static and dynamic data can be developed by generating the joint conditional probability P(AǀB,C) where A is the simulation event (k(u), u ϵ Reservoir), B is the information from the “hard data” and C is the information from the production data. When modeled accurately, any sampling from the conditional distribution would produce accurate static and dynamic characteristics. When a permanence of ratio hypothesis is employed, the conditional probability P(AǀB,C) can be expressed in terms of P(A), P(AǀB), and P(AǀC). The first term, P(A), is known given the hard data and assuming stationarity, the probability P(AǀB) can be derived by kriging via the model for spatial covariance. The term P(AǀC) relates the permeability field to the dynamic data and is difficult to compute directly and consequently an iterative calibration procedure should be implemented (as was implemented in Kashib).

A Markov chain composed of the iterative steps l = 1, . . ., L such that the outcome of the indicator RV I (u) at step l + 1 is dependent only on the outcome at the previous step l. The chain is parameterized by a dynamic factor r D є XIV that quantifies the probability of transitioning from indicator category k at step l to the category k at step l + 1 given the historic production data. (You can read further about the parametrization in Kashib.)

Types of dynamic data used in reservoir engineering can be broken down into five categories: geomechanical, fluid, production, tracer, and well test.

## References

1. Wikipedia. 2014. Dynamic data (11 August 2014 revision). http://en.wikipedia.org/wiki/Dynamic_data (accessed 21 October 2014).
2. Kashib, T., and Srinivasan, S. 2003. Iterative Integration of Dynamic Data in Reservoir Models. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 5-8 October. SPE-84592-MS. http://dx.doi.org/10.2118/84592-MS.

## Noteworthy papers in OnePetro

Blanc, G., Guerillot, D., Rahon, D., et al. 1996. Building Geostatistical Models Constrained by Dynamic Data - A Posteriori Constraints. Presented at the European 3-D Reservoir Modelling Conference, Stavanger, Norway, 16-17 April. SPE-35478-MS. http://dx.doi.org/10.2118/35478-MS.

Cobena, R.H., Aprilian, S.S., Datta-Gupta, A. 1998. A Closer Look at Non-Uniqueness During Dynamic Data Integration into Reservoir. Presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, 19-22 April. SPE-39669-MS. http://dx.doi.org/10.2118/39669-MS.

Cunha, L.B. 2004. Integrating Static and Dynamic Data for Oil and Gas Reservoir Modelling. J Can Pet Technol 43 (3). PETSOC-04-03-TN. http://dx.doi.org/10.2118/04-03-TN.

Kashib, T. and Amanetu, S. 2003. Dynamic Data Integration in Stochastic Reservoir Models. Presented at the Canadian International Petroleum Conference, Calgary, 10-12 June. PETSOC-2003-091. http://dx.doi.org/10.2118/2003-091.

Le Ravalec-Dupin, M., Roggero, F., and Froidevaux, R. 2004. Conditioning Truncated Gaussian Realizations to Static and Dynamic Data. SPE Journal 9 (4)SPE-84944-PA. http://dx.doi.org/10.2118/84944-PA.

Likanapaisal, P., Li, L., and Tchelepi, H.A. 2009. Dynamic Data Integration and Quantification of Prediction Uncertainty Using Statistical Moment Equations. Presented at the SPE Reservoir Simulation Symposium, 2-4 February, The Woodlands, Texas, USA. SPE-119138-MS. http://dx.doi.org/10.2118/119138-MS.

Ozkaya, S.I., Siyabi, S. 2008. Detection of Fracture Corridors from Dynamic Data by Factor Analysis. Presented at the SPE Saudia Arabia Section Technical Symposium, Al-Khobar, Saudi Arabia, 10-12 May. SPE-120812-MS. http://dx.doi.org/10.2118/120812-MS.

Roggero, F., Mezghani, M., Hu, L.Y., Le Ravalec-Dupin, M., et al. 2002. Constraining Stochastic Reservoir Models to Dynamic Data: An Integrated Approach. Presented at the 17th World Petroleum Congress, Rio de Janeiro, 1-5 September. WPC-32162.

Verga, F.M., Giglio, G., Masserano, F., et al. 2001. Calibration of Fractured Reservoirs with Dynamic Data. Presented at the SPE Reservoir Simulation Symposium, Houston, 11-14 February. SPE-66395-MS. http://dx.doi.org/10.2118/66395-MS.

Zabalza-Mezghani, I., Mezghani, M., and Blanc, G. 2001. Constraining Reservoir Facies Models to Dynamic Data - Impact of Spatial Distribution Uncertainty on Production Forecasts. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 30 September-3 October. SPE-71335-MS. http://dx.doi.org/10.2118/71335-MS.