EVALUATION OF IN-FILL WELL PLACEMENT AND OPTIMIZATION USING EXPERIMENTAL DESIGN AND GENETIC ALGORITHM

EVALUATION OF IN-FILL WELL PLACEMENT AND OPTIMIZATION USING EXPERIMENTAL DESIGN AND GENETIC ALGORITHM

Abstract:
The efficient placement and optimization of in-fill wells play a crucial role in maximizing hydrocarbon recovery from mature oil and gas reservoirs. This study presents an approach to evaluate and optimize in-fill well placement using a combination of experimental design and genetic algorithm techniques.

To begin with, an experimental design methodology is employed to create a set of representative reservoir models that capture the reservoir heterogeneity and uncertainty. These models are generated by varying the input parameters such as porosity, permeability, and fault locations within realistic ranges.

Subsequently, a genetic algorithm is utilized to optimize the placement of in-fill wells within the generated reservoir models. The genetic algorithm evolves a population of potential well locations by iteratively applying genetic operators such as selection, crossover, and mutation. The fitness function used in the genetic algorithm is based on the objective of maximizing the cumulative oil production while considering constraints such as drilling cost and well spacing.

The optimization process iteratively refines the well placement strategy by exploring different combinations of well locations and identifying the ones that yield the highest oil recovery. The genetic algorithm’s ability to handle large parameter spaces and non-linear relationships makes it well-suited for optimizing complex in-fill well placement problems.

The results of the evaluation and optimization process provide insights into the impact of different reservoir characteristics on in-fill well placement and the corresponding hydrocarbon recovery. The study also demonstrates the effectiveness of the experimental design and genetic algorithm approach in identifying optimal well placement configurations.

In conclusion, this research contributes to the field of reservoir engineering by proposing a methodology for evaluating and optimizing in-fill well placement using experimental design and genetic algorithm techniques. The approach can assist in decision-making processes related to reservoir development and maximizing hydrocarbon recovery in mature fields. Future work can focus on incorporating additional factors such as water flooding and reservoir dynamics to further enhance the optimization process.

EVALUATION OF IN-FILL WELL PLACEMENT AND OPTIMIZATION USING EXPERIMENTAL DESIGN AND GENETIC ALGORITHM. GET MORE OIL AND GAS/PETROLEUM ENGINEERING PROJECT TOPICS AND MATERIALS

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