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Optimization of well placement in complex carbonate reservoirs using artifical intelligence

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2004
Uraz, İrtek
This thesis proposes a framework for determining the optimum location of an injection well by using an inference method, Artificial Neural Networks and a search algorithm to create a search space and locate the global maxima. Theoretical foundation of the proposed framework is followed by description of the field for case study. A complex carbonate reservoir, having a recorded geothermal production history is used to evaluate the proposed framework ( Kizildere Geothermal field, Turkey). In the proposed framework, neural networks are used as a tool to replicate the behavior of commercial simulators, by capturing the response of the field given a limited number of parameters (Temperature, pressure, injection location and injection flow rate) as variables. A study on different network designs is followed by introduction of a search algorithm to generate decision surfaces. Results indicate that a combination of neural networks and an optimization algorithm (explicit search with variable stepping) to capture local maxima can be used to locate a region or a location for optimum well placement. Results also indicate shortcomings and possible pitfalls associated with the approach. With the provided flexibility of the proposed workflow, it is possible to incorporate various parameters including injection flow rate, temperature and location. For the field of study (Kizildere), optimum injection well location is found to be in the south-eastern part of the field. Specific locations resulting from the workflow indicated a consistent search space, having higher values in that particular region. When studied with fixed flow rates (2500 and 4911 m 3 /day), search run through the whole field located two locations which are in the very same region; thus resulting with consistent predictions. Further study carried on by incorporating effect of different flow rates indicates that the