An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling

Predicting the rate of penetration (ROP) is critical for drilling optimization because maximization of ROP can greatly reduce expensive drilling costs. In this work, the typical extreme learning machine (ELM) and an efficient learning model, upper-layer-solution-aware (USA), have been used in ROP prediction. Because formation type, rock mechanical properties, hydraulics, bit type and properties (weight on the bit and rotary speed), and mud properties are the most important parameters that affect ROP, they have been considered to be the input parameters to predict ROP. The prediction model has been constructed using industrial reservoir data sets that are collected from an oil reservoir at the Bohai Bay, China. The prediction accuracy of the model has been evaluated and compared with the commonly used conventional artificial neural network (ANN). The results indicate that ANN, ELM, and USA models are all competent for ROP prediction, while both of the ELM and USA models have the advantage of faster learning speed and better generalization performance. The simulation results have shown a promising prospect for ELM and USA in the field of ROP prediction in new oil and gas exploration in general, as they outperform the ANN model. Meanwhile, this work provides drilling engineers with more choices for ROP prediction according to their computation and accuracy demand

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

Rate of penetration management is a matter of importance in drilling operations and it has been used in some research studies. Although conventional approaches for rate of penetration management are mainly focused on analytical and semi-analytical models, several correlations have also been developed for this purpose. The history of rate of penetration management studies extends back more than half a century and ever since, research interest in this concept has never declined, making it a focus of industry and academic studies. In this article, some of these studies are reviewed to achieve a better understanding of rate of penetration management concept, its financial benefits and also its research capacities. This review reveals the most common rate of penetration management methods which applied analytical, semi-analytical and empirical correlations in different fields around the world. In other words, the main purpose of this study is to evaluate the research studies in which different models and correlations have been used as the main approach for rate of penetration management. Based on the results of this review, the best models for performing rate of penetration management studies and the best objective functions for optimization algorithms are introduced.Cited as: Najjarpour, M., Jalalifar, H., Norouzi-Apourvari, S. A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models. Advances in Geo-Energy Research, 2021, 5(3): 252-273, doi: 10.46690/ager.2021.03.0

Assessment and Modelling of Wear prediction and Bit Performance for Roller Cone and PDC Bits in Deep Well Drilling

Drilling is one of the important aspects in the oil and gas industry due to the high demand for energy worldwide. Drilling time is considered as the major part of the operations time where the penetration rate (ROP) remains as the main factor for reducing the time. Maximizing ROP to lower the drilling cost is the main aim of operators. However, high ROP if not controlled may impact on the well geometry in terms of wellbore instability, cavities, and hole diameter restrictions. Accordingly, more time is needed for the other operations that follow such as: pool out of hole (POOH), casing running, and cementing. Bit wear is considered as the essential issue that influences in direct way on the bit performance and reduce ROP. Predicting the abrasive bit wear is required to estimate the right time when to POOH to prevent any costly job to fish any junk out to the surface. The two-common types of bits are considered in the research, rock bits (roller cone bits) and Polycrystalline Diamond Compact bits (PDC). This study focuses more on PDC bits because about 60% of the total footage drilled in wells worldwide were drilled by PDC bits and this is expected to reach 80% in 2020. The contribution of this research is to help reducing the drilling cost by developing new tools not to estimating the time when to POOH to surface but also to measure the wear and enhance the accuracy of prediction the bit efficiency. The work is broken down into four main stages or models to achieve the objective: The first stage; estimating of the rock abrasiveness and calculate the dynamic dulling rate of the rock bit while drilling. The second stage; estimating the PDC abrasive cutters wear by driving a new model to determine the mechanical specific energy (MSE), torque, and depth of cut (DOC) as a function of effective blades (EB). The accuracy of the predicted wear achieves 88% compared to the actual dull grading as an average for bits used in five wells. The third stage; modifying the previous MSE tool to develop a more accurate approach; effective mechanical specific energy (EMSE), to predict the PDC bit efficiency in both the inner and outer cone to match the standard bit dulling. The fourth stage; predicting ROP while PDC drilling in hole by accounting three parts of the process: rock drillability, hole cleaning, and cutters wear. The results achieve an enhancement of about 40% as compared to the available previous models. Consequently, the developed models in this study provide a novelty on understanding in more details the bit rock interface process and gain an idea of the relationship between the drilling parameters to enhance the bit performance and avoid damaging the bit. This is basically about optimisation the controllable factors such as: weight on bit (WOB), rotary speed (RPM), and flow rate. The result is the reduction in time losses and the operations cost. To ensure reliability and consistency of the proposed models, they were validated with several vertical oil wells drilled in Libya. The results from the validation of the models are consistent with the real field data. The research concludes that the developed models are reliable and applicable tool for both: to assist decision-makers to know when to pull the bit out to surface, and also to estimate the bit performance and wear