Data is a central part of any dynamic simulation model construction or update. In the age of intelligent fields, a tremendous amount of data is generated on a real-time basis, which calls for frequent simulation model updates. This process improves the accuracy and predictability of simulation models. To enhance the efficiency of the process, a genuine software utility has been developed.
The current practice is to manually append quality checked (quality controlled) observed data to new observed data retrieved from the company database. At first, engineers need to identify new pressures for each well. Then, the extracted pressures are added to the existing pressure data in the simulation model. A similar process is also carried out for oil production rates (OPR) and water or gas injection rates (WIR or GIR). Such processes are prone to human errors and require tedious data quality checks.
Manually updating simulation models depends on three factors: update frequency, number of wells, and available human resources. For example, a simulation model with about 700 wells might take an engineer as much as a full working week for data retrieval, integration of existing and new observed data, and quality checking. In contrast, it takes the developed utility only a few seconds to perform the same task. In addition, automating the process eliminates the QC work needed to check syntax errors introduced during the manual update.
This paper presents the validity and fidelity of the developed utility for dynamic simulation model update. The conceptual examples shared in this paper demonstrate the benefits of this genuine software utility in terms of data integration and efficiency.
History matching is the act of calibrating a reservoir model until it closely reproduces the past behavior of a reservoir (Maucec et al. 2007). Rwechungura et al. (2011) stated that the main reason for performing history matching is to enable the creation of prediction cases that can reliably forecast future performance of a reservoir, and thereby enhance production optimization and economic hydrocarbon recovery. Figure 1 demonstrates a schematic of history matched simulation model results compared to historical data and how it is well fitted.
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