Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Predicting and optimizing the drilling rate of penetration (ROP) poses a significant challenge due to its dependence on various factors, prompting increased attention towards achieving precise ROP estimations given its direct influence on overall drilling expenses. Among the factors influencing ROP, the driving mechanism of the bottom hole assembly (BHA) plays a pivotal role. Motorized BHAs offer versatile applications beyond directional drilling, including optimization of ROP and mitigation of downhole vibration. While several models have been proposed to forecast ROP for rotary and rotary steerable system BHAs, limited attention has been directed towards motorized BHAs. In this study, a novel artificial intelligence (AI)-based model employing gradient boosting regression (GBR) was developed to predict ROP for motorized BHAs, leveraging surface drilling parameters, mud characteristics, and motor output features. The dataset used for model training, validation, and testing was sourced from six wells spanning two adjacent fields in the Egyptian Western Desert, comprising over 5,800 data points. Mean absolute percentage error (MAPE) served as an evaluation metric for prediction accuracy, while the correlation coefficient (R) quantified the extent of agreement between real and predicted ROP values. Results demonstrated that the GBR model accurately estimated ROP for motorized BHAs, exhibiting a high correlation (R of 0.95) between predicted and real values. The GBR-based model consistently performed well without exhibiting underfitting or overfitting issues. Furthermore, the developed model enables exploration of the impact of different drilling parameters on motorized BHA ROP, thereby facilitating ROP optimization, reduction of open hole exposure duration, and overall drilling cost minimization.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - GOTECH Conference 2024
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781959025405
DOIs
StatePublished - 2024
Event2024 SPE Gas and Oil Technology Conference, GOTECH 2024 - Dubai, United Arab Emirates
Duration: 7 May 20249 May 2024

Publication series

NameSociety of Petroleum Engineers - GOTECH Conference 2024

Conference

Conference2024 SPE Gas and Oil Technology Conference, GOTECH 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period7/05/249/05/24

Bibliographical note

Publisher Copyright:
© 2024, Society of Petroleum Engineers.

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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