دانشگاه آزاد اسلامی واحد تهران جنوب

صفحه اصلی / دومین کنفرانس ملی فناوری های نوین در انرژی و مواد

Using Neural Network Models to predict Equivalent Circulation Density (ECD)

نویسندگان :

Chinar Aliomar¹ Ahmad Adib²

1- Islamic Azad University, South Tehran Branch, Tehran, Iran 2- Islamic Azad University, South Tehran Branch, Tehran, Iran

کلمات کلیدی :

ECD،Rate of penetration،Drilling cost،Bit،ANN

چکیده :

The most important criterion for reducing the cost of drilling is prediction of ROP from the current available data. ROP performs rock bit interaction which appertain rock compressive strength and bit aggressively. ROP prediction is complex process because of too many variables are included, their input parameters are often not readily available, and their relationships are complex and not easily modeled. So, the application of Neural Network is suggested in this study. To predict the rate of penetration Some new methodology has been developed like using the Artificial Neural Network (ANN). Application of the new network models would then be used for selecting the best parameters for an optimal drilling strategy based on field data. Rock bit interactions in the field as a function of rock mechanical property parameters was achieved by predicting ROP which relates to rock compressive strength and bit aggressively; as well as TWR which relates to rock abrasiveness and wear resistance. Based on field data, the prediction of rock mechanical property parameters can be accomplished by the use of a neural network as an alternative prediction and optimization method.