A Data Driven Approach of ROP Prediction and Drilling Performance Estimation

Han, Jiahang; Sun, Yanji; Zhang, Shaoning

doi:10.2523/19430-ms

Cited by 17 publications

(2 citation statements)

References 6 publications

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“…If the experienced ROP deviates from the model's prediction this can be used as an indicator for bit balling. Another example utilizing machine learning for ROP prediction is presented in [63]. A Long-Short-Term-Memory Neural Network model is deployed, utilizing additional features for training -the type of the drill bit, formation properties, and the rheological properties of the drilling mud are explored to improve model accuracy.…”

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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“…This characteristic has great advantages in dealing with large-scale multi-dimensional data and time-series problems. In the petroleum industry, LSTM was used to perform secondary generation of well logging data [36], forecast production decline of multiple wells [37] and predict rate of penetration (ROP) based on recorded drilling data [38]. However, this method has not been used for the prediction of water saturation distribution in reservoir yet.…”

Section: Introductionmentioning

confidence: 99%

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

et al. 2019

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Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

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Predicting Rate of Penetration of Horizontal Wells Based on the Di-GRU Model

Pan,

Song,

et al. 2024

Rock Mech Rock Eng

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A Data Driven Approach of ROP Prediction and Drilling Performance Estimation

Cited by 17 publications

References 6 publications

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

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

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Predicting Rate of Penetration of Horizontal Wells Based on the Di-GRU Model

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