Author: Ahmed, Yahia El-Sayed Khamis./ Title: Real Time Prediction of Rate of Penetration (ROP) in Drilling Oil and Gas Wells using Machine Learning Techniques /

Search In this Thesis

العنوان

Real Time Prediction of Rate of Penetration (ROP) in Drilling Oil and Gas Wells using Machine Learning Techniques /

المؤلف

Ahmed, Yahia El-Sayed Khamis.

هيئة الاعداد

باحث / يحيي السيد خميس

مشرف / محمد مصطفى سليمان

مشرف / عادل محمد سالم

مناقش / احمد زكريا نوح

الموضوع

Rate of Penetration. Machine Learning. K-Nearest Neighbors.

تاريخ النشر

2023.

عدد الصفحات

i-xv, 72 p. :

اللغة

الإنجليزية

الدرجة

ماجستير

التخصص

الهندسة

تاريخ الإجازة

1/11/2023

مكان الإجازة

جامعة السويس - المكتبة المركزية - هندسة البترول

الفهرس

Only 14 pages are availabe for public view

from

205

from

205

Abstract

The goal of this research is to introduce new data-driven models used to predict Rate of penetration using different parameters such as (Depth, weight on bit(WOB), Revolution per minute(RPM), Torque(T), Standpipe pressure (SPP), Flow in pump(pumping flow rate(Q) ), Mud weight, Hours on bit (HOB), Revolutions on bit, Bit diameter, Total flow area(TFA), Pore pressure, Overburden pressure, and Pit volume). Data-driven models are built using eight different machine learning techniques, using 1771 raw actual field data collected from a vertical well.