--> Abstract: Deep Water Reservoir Characterization Using Transient Well Test Data, by Hong Tang, Fengjun Zhang, and Kathy Mabe; #90124 (2011)

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Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA

Deep Water Reservoir Characterization Using Transient Well Test Data

Hong Tang1; Fengjun Zhang1; Kathy Mabe1

(1) Chevron Africa and Latin America Exploration and Production Company, houston, TX.

Lucapa field has oil reservoirs trapped within Middle Miocene sandstones associated with a salt dome located in Block 14, Angola. Developing the reservoir is expected to be challenging due to a combination of complex geology, location within the Congo Canyon, and data quality concerns. Seismic data washout zones result from shallow sediment and fluid heterogeneities, in addition to a complex structural setting (i.e., graben faults and high angle bed dips surrounding the salt dome). Deposition is interpreted to have occurred in a deepwater turbiditic slope valley canyon system. The channel deposits exhibit high heterogeneity as a result of high permeability (up to 7 Darcy) channel lags and injectites. To date, there are only three well penetrations in the field which are considered representative of the reservoir deposits. As a result, integrating well transient data (DST) is crucial for benchmarking well log permeability, calibrating static reservoir model simulations, and ensuring a reasonable dynamic model.

Well transient data modeling is conducted to calculate critical reservoir information, including, but not limited to, distance-to-flow-barrier, cumulative KH, , skin, Kv/Kh, and shape of flow regimes. Different multivariable regression methods are tested to compare and calibrate log derived KH with DST calculated KH.

To match DST data, two methods are tested. The first method is based on the assumption that any DST mismatch is due to perturbation among permeability stochastic realizations. An innovative modelling workflow is used to fine-tune permeability realization within the radius of investigation, while still honoring the well interpretation and local geological trends. An alternative assumption is that the uncertainty of the proportion and geometric distribution of high permeability zones causes the DST mismatch. By adjusting the proportion and distribution of these pebbly lithofacies, we are able to match DST data, and better honor production decline rate and water breakthrough from direct analogue data. This information is then used in the selection of the appropriate models used for final production forecasting.

Both methods are able to efficiently match well test history without invoking a permeability multiplier within two iterations. Integration of DST data within the reservoir model is considered crucial, and a best practice for deepwater green field development in similar areas of investigation.