Digital Twins for Well Planning and Bit Dull Grade Prediction

Abstract

In this work, we develop and apply an offset well data analysis framework to generate a digital twin that is representative of bit state. We also strive to produce performance maps for well planning. Our workflow involves three major elements: 1) offset well data analysis to generate detailed depth-based and time-based statistics 2) computation of wear on bit and efficient weight-on-bit (WOB) versus depth for all the runs 3) automated machine learning to generate an accurate predictive model for bit dull grade to deploy for real-time operations. We define and calculate the efficient WOB as the minimum WOB needed to fully engage all cutters on the bit and maximize depth of cut. The efficient WOB changes with both bit state and formation strength, so an indication of bit wear can be used to predict formation strength. Deployment of the bit dull grade predictor enables the drilling crew to monitor the bit state while drilling and decide whether a reduction in ROP is due to a worn bit or other factors. Application to field cases from US Land yielded promising results. After computing the bit wear, performance maps for different bit states (e.g., sharp vs worn bits) are created. These performance maps help identify the optimal drilling parameters in well planning.

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

“…ZrO 2 nanoparticle aggregation is observed for all composite materials irrespective of ZrO 2 concentration. Te increased surface area of nanoparticles is evidence of their accumulation in the composite structure [29]. Nanoparticle aggregation was reported for AA6063/2ZrO 2 composite.…”

Construction of a digital twin model for vertical storage tank deformation assessment using terrestrial laser scanning technology

“…A digital model used for tank inspection work should include a complete set of processes that take into account automation, accuracy, and standardization. A digital twin (DT) digitally describes the state of a physical entity [10][11][12][13] and can improve the accuracy, efficiency, and reliability of the inspection process [14,15]. TLS has extensive applications in the construction of DT models, providing high-precision geometric information and digital validation tools, making it one of the primary techniques for digital model construction [16][17][18].…”