Cloud-Based ROP Prediction and Optimization in Real-Time Using Supervised Machine Learning

Singh, Kumar Abhishek; Yalamarty, Sai Sharan; Kamyab, Mohammadreza; Cheatham, Curtis

doi:10.15530/urtec-2019-343

Cited by 25 publications

(4 citation statements)

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“…Therefore, the authors utilized a Random Forest Regressor which is less prone to overfitting and obtained the best performance, compared to various machine learning algorithms such as Support Vector Machines, K-Nearest Neighbours and an Artificial Neural Networks. Singh et al [66] developed a deployable real-time solution for ROP prediction, evaluating the performance of eight different algorithms. The algorithms where trained on a dataset containing 50 wells.…”

Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

et al. 2020

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Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability. INDEX TERMS Bottom hole assembly, oil drilling, printed component board assembly, dynamic environments, failure modes, condition based maintenance, diagnostics, prognostics, machine learning, artificial intelligence.

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Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

et al. 2020

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“…Traditional models are those ROP models which try to establish a mathematical equation among the drilling parameters (Shi et al, 2016 ;Barbosa et al, 2019). Singh et al, (2019) stated that determining a correlation or linear relationship among the drilling parameters with ROP is very difficult. Statistical models have some similarities with the traditional models, necessity of preselection a model for ROP as a function of drilling parameters, and with main difference was the statistical models did not model the physics of drill bit mechanism and the formation and bit interactions (Barbosa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Since ROP is the main target to be optimized, then the use of supervised machine learning become essential. A supervised machine learning model for ROP prediction was developed that is efficient for use with real data (Singh et al, 2019), supervised machine learning provides a target from series of training dataset with sets of predictor features (Noshi & Schubert, 2018). (Hegde, Chiranth, Wallace, Scott, 2015); Barbosa et al, (2019) compared traditional models of ROP calculations with machine learning techniques concluded that a higher of accuracy could be achieved in ROP prediction with intelligent techniques.…”

Section: Introductionmentioning

confidence: 99%

“…No lithology data available for this project. Singh et al, (2019), in supervised machine learning the goal is to approximate the mapping function so well that that you have new input data you can predict the output variables for that data. Fernandes et al, (2018)had mentioned the ensemble learning family algorithms build a model by training several relatively simple base models and then combine them to create a more predictive model, the most well-known ensemble learning algorithms use bootstrap aggregation, known as Bagging, Random Forest, and Gradient Boosting.…”

Section: Introductionmentioning

confidence: 99%

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ROP Prediction with Supervised Machine Learning; a Case Study

Darmawan

Irawan

2022

J. earth energy eng.

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Optimum drilling penetration rate, known as the rate of penetration (ROP) has played a big role in drilling operations. Planning the well ROP always becomes a challenge for drilling engineers to calculate the drilling time needed for the section. Optimum ROP is achieved when the time to drill the section is as planned. Many empirical approaches were develop to model the ROP based on the drilling parameters, and might not always match the actual ROP. In some cases, the actual ROP was slower than planned, which may increase the drilling cost, which needs to be avoided. Hence, some approaches using artificial intelligent (AI), and supervised machine learning have been develop to overcome it. Supervised machine learning is used to develop a ROP model and ROP prediction for one of the development fields, based only on two wells drilling parameters data. The model was train using Gradient Boosting, Random Forest, and Support Vector Machine. Drilling parameter test data then is used to validate the model. The model of Random Forest shows a good or promising result with R2 of 0.90, Gradient Boosting shows R2 of 0.86, and Support Vector Machine with R2 0.72. Based on the models generated, the Random Forest has shown good trend which could be used for modeling ROP in the future development wells.

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