Kayaç Özelliklerine Bağlı Olarak Kayaç Delinebilirliğinin Yapay Sinir Ağları (YSA) Metodu ile Tahmini

Bu çalışmanın amacı; kayaçların mekanik, indeks ve petrografik özelliklerinden yararlanarak pratik bir delinebilirlik indeks modeli (DRI) geliştirmektir. DRI’nın tahmin edilmesi için geliştirilen modellerde yapay sinir ağları (YSA) kullanılmıştır. Bu amaçla, laboratuvarda farklı kayaçlar üzerinde yapılan delinebilirlik deney verileri kullanılmıştır. Ayrıca kayaçların; mekanik (tek eksenli basınç ve Brezilian çekme dayanımı) ve indeks (Schmidt sertlik ve nokta yükü dayanımı) özelliklerinin yanı sıra petrografik analiz (ortalama tane boyu, eş değer kuvars içeriği (%), çimentolanma derecesi) sonuçları da YSA modellemesi için kullanılmıştır. DRI ve kayaç özellikleri arasındaki korelasyon incelenmiş ve yüksek korelasyona sahip değişkenler YSA’na girdi parametresi olarak seçilmiştir. Buna göre 14 veri ağın eğitimi için, 7 veri ise test için kullanılmış ve en anlamlı sonucu veren model belirlenmiştir. Testler sonucunda tahmin edilen DRI sonuçları ile deneylerden elde edilen DRI sonuçlarının karşılaştırması yapılmıştır. Sonuç olarak YSA ile modellenen DRI sonuçlarının deneysel verilerden elde edilen sonuçlara oldukça yakın olduğu görülmüştür.

Prediction of Drillability of Rocks Based on Rock Properties Using Artificial Neural Networks (ANN)

The objective of this paper is to develop a practical model for predicting the drilling rate index (DRI) based on mechanical, index and petrographic properties of rocks. Artificial neural network (ANN) is employed in the models developed for predicting the DRI. For this purpose, experimental data obtained from drillability test carried out on different rocks in the laboratory were used. Furthermore, in addition to the mechanical (i.e. uniaxial compressive and Brazilian tensile strengths) and index (i.e. Schmidt hardness and point load index) properties, the results of petrographic analyses (i.e. mean grain size, equivalent quartz content, degree of cementation) were also used for the ANN modelling. The correlations between the DRI and rock properties were evaluated, and the parameters having high correlations were selected as input parameters for the ANN model. Accordingly, 14 data were used for network training and 7 data were used for testing, and the most meaningful was determined. The estimated DRI values from the ANN models were compared with the experimental DRI data. In conclusion, the DRI values estimated by the ANN models were found to be close to those obtained from experiments.

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