During a field development process, one of the parameters used to decide the in fill well locations is permeability-thickness (kh) map. This map can provide us with overall trends in the conductivity of the formation as well as information about optimizing water flood patterns. Rosneft routinely uses kh maps as a reservoir management tool. In addition to developing the well locations, it also uses the kh values to optimize the artificial lift design so that the wells are produced efficiently.

One difficulty typically observed in generating kh maps is a prominent display of "bull's eyes." The values of kh can change dramatically from well to well, which causes problems in interpolation of these values. Some times, because of large discrepancies, the overall patterns are hard to discern and well planning is more difficult.

In the proposed work, we developed a procedure for capturing the trends in kh maps by removing the bull's eyes. The kh values are determined by two methods: use the production data and using simplified procedure, determine the value of kh, or evaluate well test data and determine the kh values. In the first step, we developed a process of reconciling the well test data with the production data by adjusting the kh values and skin factors so that the productivity index can be maintained. In the second step, we assumed that uncertainty exists in determining the true kh value at each well location due to interpretation and resolution of data. Instead of strictly honoring the kh values at each location, using error kriging approach, we recalibrated the kh values so that the new kh maps are smoother and without bull's eyes, and are able to define the overall trends much better. We also ensured that the productivity index matches correctly. Further, by examining the productivity index as a function of time (based on production data), we are able to determine how the skin factor changes as a function of time, which provides valuable information about potential damage at the well location. The procedure was validated by applying it to a large oil field located in Siberia with successful application of in-fill well program.

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