MODELING AND PREDICTION OF RATE OF PENETRATION FOR DEVIATED WELLS
Date
2020-12-15
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Type
Thesis
Degree Level
Doctoral
Abstract
Setting up a drilling plan and efficiently drilling a new well requires analysis of offset well data to properly determine bottom hole assembly, drill bit for a given section, drilling fluids, and operational parameters such as weight on bit (WOB) and drill-string rotary speed. The purpose of drilling planning is to optimize the Rate of Penetration (ROP) by reducing non-productive time (NPT). In this thesis, an accurate semi-principle model is developed for predicting ROP in deviated drilling sections with the application of Polycrystalline Diamond Compact (PDC) bits. This general predictive model can be used for both well planning and in exploration wells in offshore operations to optimize drilling parameters.
The proposed semi-principle model is inspired by Bourgoyne and Young’s (B&Y) model and follows an exponential form with 10 different drilling functions to account for various factors affecting ROP. The original B&Y model does not apply to PDC bits and deviated wells, and its parameters are obtained using data from the entire well. This model is extended to PDC bits. It is also demonstrated in this thesis that a different predictive model should be obtained for each formation. On top of the factors included in the B&Y model, the proposed ROP model accounts for parameters such as downhole motor, equivalent circulating density (ECD), and wellbore inclination. In particular, the impact of equilibrium cuttings bed thickness and downhole cutting concentration on ROP are incorporated. Parameters of the model are obtained by use of multiple regression analysis using field data. The model is then incorporated in a framework to obtain an optimal well plan for a new well. It can be used for prescribing optimal operational parameters for real-time drilling operations. The importance of obtaining a formation-based ROP model is tested and verified with field data and an algorithm to determine the parameters for new data is provided.
Another formation-based predictive model for ROP prediction is developed in this thesis using Artificial Neural Networks (ANN). ANN is combined with the proposed semi-principle model. Physics of drilling systems is incorporated into input variables that influence ROP. Drilling functions are then considered as input variables to the ANN. A comparative experiment is also performed between the proposed ANN-ROP models and conventional ANN-ROP models.
Field data in this work corresponds to tens of offshore wells. The proposed models are tested with data collected from the 12 ¼ and 8 ½ inches drilling sections, the longest drilling sections of these wells. The prediction performance of the proposed models is also evaluated in various formations in several test wells across the field. The results indicate that the proposed semi-principle model is able to predict the drilling ROP with 10% relative error and the proposed ANN-ROP models are able to predict ROP with average less than 1% relative error in training wells and less than 5% relative error in test wells.
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Keywords
Rate of penetration, Semi-principle models, Semi-empirical models, Offset well data, Well-planning
Citation
Degree
Doctor of Philosophy (Ph.D.)
Department
Mechanical Engineering
Program
Mechanical Engineering