Downhole data correction for data-driven rate of penetration prediction modeling

Abstract Theoretically, rate of penetration (ROP) model is the basic to drilling parameters design, ROP improvement tools selection and drill time & cost estimation. Currently, ROP modelling is mainly conducted by two approaches: equation-based approach and machine learning approach, and machine learning performs better because of the capacity in high-dimensional and non-linear process modelling. However, in deep or deviated wells, the ROP prediction accuracy of machine learning is always unsatisfied mainly because the energy loss along the wellbore and drill string is non-negligible and it's difficult to consider the effect of wellbore geometry in machine learning models by pure data-driven methods. Therefore, it's necessary to develop robust ROP modelling method for different scenarios. In the paper, the performance of several equation-based methods and machine learning methods are evaluated by data from 82 wells, the technical features and applicable scopes of different methods are analysed. A new machine learning based ROP modelling method suitable for different well path types was proposed. Integrated data processing pipeline was designed to dealing with data noises, data missing, and discrete variables. ROP effecting factors were analysed, including mechanical parameters, hydraulic parameters, bit characteristics, rock properties, wellbore geometry, etc. Several new features were created by classic drilling theories, such as downhole weight on bit (DWOB), hydraulic impact force, formation heterogeneity index, etc. to improve the efficiency of learning from data. A random forest model was trained by cross validation and hyperparameters optimization methods. Field test results shows that the model could predict the ROP in different hole sections (vertical, deviated and horizontal) and different drilling modes (sliding and rotating drilling) and the average accuracy meets the requirement of well planning. A novel data processing and feature engineering workflow was designed according the characteristics of ROP modelling in different well path types. An integrated data-driven ROP modelling and optimization software was developed, including functions of mechanical specific energy analysis, bit wear analysis and predict, 2D & 3D ROP sensitivity analysis, offset wells benchmark, ROP prediction, drilling parameters constraints analysis, cost per meter prediction, etc. and providing quantitative evidences for drilling parameters optimization, drilling tools selection and well time estimation.

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Data-driven intervention-level prediction modeling for academic performance

Proceedings of the Seventh International Conference on Information and Communication Technologies and Development - ICTD '15 ◽

10.1145/2737856.2738012 ◽

2015 ◽

Cited By ~ 4

Author(s):

Mvurya Mgala ◽

Audrey Mbogho

Keyword(s):

Academic Performance ◽

Data Driven ◽

Prediction Modeling ◽

Intervention Level

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Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2017.09.020 ◽

2017 ◽

Vol 159 ◽

pp. 295-306 ◽

Cited By ~ 38

Author(s):

Chiranth Hegde ◽

Hugh Daigle ◽

Harry Millwater ◽

Ken Gray

Keyword(s):

Data Driven ◽

Rate Of Penetration

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Micro-Testing While Drilling for Rate of Penetration Optimization

Volume 11: Petroleum Technology ◽

10.1115/omae2020-18838 ◽

2020 ◽

Author(s):

Magnus Nystad ◽

Alexey Pavlov

Keyword(s):

Noisy Data ◽

Optimization Method ◽

Extremum Seeking ◽

Data Driven ◽

High Fidelity ◽

Drilling Process ◽

Optimization Scheme ◽

Rate Of Penetration ◽

Drilling Parameters ◽

Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. To avoid violation of constraints, the algorithm is adjusted with a combination of a predictive and a reactive approach. This method of constraint handling is demonstrated for a maximal limit imposed on the surface torque, but the method is generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested on a high-fidelity drilling simulator. The simulated scenarios show the method’s ability to steer the system to the optimum and to handle constraints and noisy data.

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Hybrid Data Driven Drilling and Rate of Penetration Optimization

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2020.108075 ◽

2020 ◽

pp. 108075

Author(s):

Ammar M. Alali ◽

Mahmoud F. Abughaban ◽

Beshir M. Aman

Keyword(s):

Data Driven ◽

Rate Of Penetration ◽

Hybrid Data

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Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization

10.2118/202184-ms ◽

2021 ◽

Author(s):

Asad Mustafa Elmgerbi ◽

Clemens Peter Ettinger ◽

Peter Mbah Tekum ◽

Gerhard Thonhauser ◽

Andreas Nascimento

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Real Time ◽

Predictive Models ◽

Data Driven ◽

Machine Learning Techniques ◽

Time Rate ◽

Rate Of Penetration ◽

Drilling Parameters ◽

Learning Techniques

Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.

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