چكيده به لاتين
Permeabilty and lithology are key parameters related to reservoir properties. In fact, there are no precise solutions to reservoir engineering issues without access to accurate permeability and lithology information. So far, the oil industry has been trying to achieve permeability values through laboratory measurements or through well tests. Although these are presented as solutions, these do not have sufficient and reliable methods to describe the reservoir and have operational problems with. On the other hand, well logs and core analysis data have been used to estimate permeability, but due to existing empirical relationships, this pattern has always not been accurate and adequate. Also, to determine and describe the lithology of the reservoir rock, methods such as direct observation of the core, well logs analysis, and seismic data are used.
Despite the limitations, recent studies have shown that there are some empirical relationships between permeability, lithology, and some petrophysical characteristics. Many regression methods have been used to determine this relationship, but they have not been sufficiently precise. In this regard, artificial intelligence algorithms have been used as reliable tools in detecting nonlinear relationships to estimate permeability and lithology descriptions using data from well logs and core analysis. The results of the research show that this method is faster than other methods of determining the permeability. Also, its cost is very low and its precision is comparable with precise methods, such as core analysis
In this thesis, well logs and cores data obtained from four wells in one of the southwest reservoirs of Iran have been used to construct data-driven models for estimating permeability and lithology. The data from three wells were used to train the models and the fourth well data was used to validate them. The models used were adaptive neuro-fuzzy inference system and support vector machine. The results show that the neural-fuzzy inference model has better performance in prediction of permeability and support vector machine model in prediction of lithology, so that in the validation step, the regression coefficient is close to one.
