Micro-Testing While Drilling for Rate of Penetration Optimization

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 (ES) is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method which gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling within ES application to drilling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method's ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

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Combining Machine Learning and Classic Drilling Theories to Improve Rate of Penetration Prediction

10.2118/202202-ms ◽

2021 ◽

Author(s):

Hongbao Zhang ◽

Baoping Lu ◽

Lulu Liao ◽

Hongzhi Bao ◽

Zhifa Wang ◽

...

Keyword(s):

Machine Learning ◽

Data Processing ◽

Cost Estimation ◽

Linear Process ◽

Parameters Optimization ◽

Data Driven ◽

Rock Properties ◽

Rate Of Penetration ◽

Modelling Method ◽

Drilling Parameters

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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Drilling Cutting Analysis to Assist Drilling Performance Evaluation in Hard Rock Hole Widening Operation

Volume 11: Petroleum Technology ◽

10.1115/omae2020-19286 ◽

2020 ◽

Author(s):

Daiyan Ahmed ◽

Yingjian Xiao ◽

Jeronimo de Moura ◽

Stephen D. Butt

Keyword(s):

Performance Enhancement ◽

Ore Deposits ◽

Drilling Process ◽

Rotary Drilling ◽

Pilot Hole ◽

Drilling Rig ◽

Drilling Performance ◽

Drilling Parameters ◽

Weight On Bit ◽

Drilling Operations

Abstract Optimum production from vein-type deposits requires the Narrow Vein Mining (NVM) process where excavation is accomplished by drilling larger diameter holes. To drill into the veins to successfully extract the ore deposits, a conventional rotary drilling rig is mounted on the ground. These operations are generally conducted by drilling a pilot hole in a narrow vein followed by a hole widening operation. Initially, a pilot hole is drilled for exploration purposes, to guide the larger diameter hole and to control the trajectory, and the next step in the excavation is progressed by hole widening operation. Drilling cutting properties, such as particle size distribution, volume, and shape may expose a significant drilling problem or may provide justification for performance enhancement decisions. In this study, a laboratory hole widening drilling process performance was evaluated by drilling cutting analysis. Drill-off Tests (DOT) were conducted in the Drilling Technology Laboratory (DTL) by dint of a Small Drilling Simulator (SDS) to generate the drilling parameters and to collect the cuttings. Different drilling operations were assessed based on Rate of Penetration (ROP), Weight on Bit (WOB), Rotation per Minute (RPM), Mechanical Specific Energy (MSE) and Drilling Efficiency (DE). A conducive schedule for achieving the objectives was developed, in addition to cuttings for further interpretation. A comprehensive study for the hole widening operation was conducted by involving intensive drilling cutting analysis, drilling parameters, and drilling performance leading to recommendations for full-scale drilling operations.

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Exploitation of Field Drilling Data with an Innovative Drilling Simulator: Highly Effective Simulation of Rotating and Sliding Mode

10.2118/202176-ms ◽

2021 ◽

Author(s):

Alexis Koulidis ◽

Vassilios Kelessidis ◽

Shehab Ahmed

Keyword(s):

Field Data ◽

Sliding Mode ◽

P Wave ◽

Drilling Process ◽

Continuous Change ◽

Rate Of Penetration ◽

Drilling Performance ◽

Drilling Parameters ◽

Rock Drillability ◽

Drilling Data

Abstract Drilling challenging wells requires a combination of drilling analytics and comprehensive simulation to prevent poor drilling performance and avoid drilling issues for the upcoming drilling campaign. This work focuses on the capabilities of a drilling simulator that can simulate the directional drilling process with the use of actual field data for the training of students and professionals. This paper presents the results of simulating both rotating and sliding modes and successfully matching the rate of penetration and the trajectory of an S-type well. Monitored drilling data from the well were used to simulate the drilling process. These included weight on bit, revolutions per minute, flow rate, bit type, inclination and drilling fluid properties. The well was an S-type well with maximum inclination of 16 degrees. There were continuous variations from rotating to sliding mode, and the challenge was approached by classifying drilling data into intervals of 20 feet to obtain an appropriate resolution and efficient simulation. The simulator requires formation strength, pore and fracture pressures, and details of well lithology, thus simulating the actual drilling environment. The uniaxial compressive strength of the rock layer is calculated from p–wave velocity data from an offset field. Rock drillability is finally estimated as a function of the rock properties of the drilled layer, bit type and the values of the drilling parameters. It is then converted to rate of penetration and matched to actual data. Changes in the drilling parameters were followed as per the field data. The simulator reproduces the drilling process in real-time and allows the driller to make instantaneous changes to all drilling parameters. The simulator provides the rate of penetration, torque, standpipe pressure, and trajectory as output. This enables the user to have on-the-fly interference with the drilling process and allows him/her to modify any of the important drilling parameters. Thus, the user can determine the effect of such changes on the effectiveness of drilling, which can lead to effective drilling optimization. Certain intervals were investigated independently to give a more detailed analysis of the simulation outcome. Additional drilling data such as hook load and standpipe pressure were analyzed to determine and evaluate the drilling performance of a particular interval and to consider them in the optimization process. The resulting rate of penetration and well trajectory simulation results show an excellent match with field data. The simulation illustrates the continuous change between rotating and sliding mode as well as the accurate synchronous matching of the rate of penetration and trajectory. The results prove that the simulator is an excellent tool for students and professionals to simulate the drilling process prior to actual drilling of the next inclined well.

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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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A Robust Rate of Penetration Model for Carbonate Formation

Journal of Energy Resources Technology ◽

10.1115/1.4041840 ◽

2018 ◽

Vol 141 (4) ◽

Cited By ~ 19

Author(s):

Ahmad Al-AbdulJabbar ◽

Salaheldin Elkatatny ◽

Mohamed Mahmoud ◽

Khaled Abdelgawad ◽

Abdulaziz Al-Majed

Keyword(s):

Compressive Strength ◽

Drilling Fluid ◽

Coefficient Of Determination ◽

Percentage Error ◽

Drilling Process ◽

Rate Of Penetration ◽

New Model ◽

Carbonate Formation ◽

Drilling Parameters ◽

Fluid Properties

During the drilling operations, optimizing the rate of penetration (ROP) is very crucial, because it can significantly reduce the overall cost of the drilling process. ROP is defined as the speed at which the drill bit breaks the rock to deepen the hole, and it is measured in units of feet per hour or meters per hour. ROP prediction is very challenging before drilling, because it depends on many parameters that should be optimized. Several models have been developed in the literature to predict ROP. Most of the developed models used drilling parameters such as weight on bit (WOB), pumping rate (Q), and string revolutions per minute (RPM). Few researchers considered the effect of mud properties on ROP by including a small number of actual field measurements. This paper introduces a new robust model to predict the ROP using both drilling parameters (WOB, Q, ROP, torque (T), standpipe pressure (SPP), uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. In addition, the relative importance of drilling fluid properties, rock strength, and drilling parameters to ROP is determined. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower (HP), where the coefficient of determination (T2) was 0.71, 0.87, 0.70, and 0.92 for WOB, RPM, T, and HP, respectively. ROP also showed a strong function of mud fluid properties, where R2 was 0.70 and 0.70 for plastic viscosity (PV) and mud density, respectively. No clear relationship was observed between ROP and yield point (YP) for more than 500 field data points. The new model predicts the ROP with average absolute percentage error (AAPE) of 5% and correlation coefficient (R) of 0.93. In addition, the new model outperformed three existing ROP models. The novelty in this paper is the application of the clustering technique in which the formations are clustered based on their compressive strength range to predict the ROP. Clustering yielded accurate ROP prediction compared to the field ROP.

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Selection of Suitable Drilling Parameters for obtaining high Rate of Penetration in Majnoon Oilfield

Iraqi Journal of Chemical and Petroleum Engineering ◽

10.31699/ijcpe.2019.1.9 ◽

2019 ◽

Vol 20 (1) ◽

pp. 65-68

Author(s):

Majid M. Majeed ◽

Ayad A. Alhaleem

Keyword(s):

Flow Rate ◽

Extensive Study ◽

High Rate ◽

Proper Selection ◽

Rate Of Penetration ◽

Technical Paper ◽

Drilling Parameters ◽

Weight On Bit ◽

Drilling Time ◽

Selection Of

Several directional wells have been drilled in Majnoon oilfield at wide variation in drilling time due to different drilling parameters applied for each well. This technical paper shows the importance of proper selection of the bit, Mud type, applied weight on Bit (WOB), Revolution per minute (RPM), and flow rate based on the previous wells drilled. Utilizing the data during drilling each section for directional wells that's significantly could improve drilling efficiency presented at a high rate of penetration (ROP). Based on the extensive study of three directional wells of 35 degree inclination (MJ-51, MJ-52, and MJ-54) found that the applied drilling parameters for MJ-54 and the bit type within associated drilling parameters to drill 36", 24", 16" and 12 1/4" hole sections is the best, although the drilling parameters in 8 1/2" hole section for MJ-51 and selected bit type are the best for future wells.

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Optimizing Directional Drilling While Simultaneously Maximizing the Whole Life Value of the Well

10.2118/207342-ms ◽

2021 ◽

Author(s):

John Martin Clegg

Keyword(s):

Performance Indicator ◽

Production Equipment ◽

Drilling Process ◽

Directional Drilling ◽

Time Curve ◽

Rate Of Penetration ◽

Drilling Parameters ◽

Drilling System ◽

The Impact

Abstract Increasingly complex wells and longer laterals present new challenges for wellbore placement and wellbore quality. There is a growing understanding of the impact of well placement and wellbore quality on the overall value of the well and on the economics of completions and production. This paper looks at how requirements have evolved and will evolve beyond simply "getting to TD" as quickly as possible and how emerging technologies can help. There is already an undercurrent of opinion that completions and production are sometimes compromised to maximize rate of penetration, but with some controversy about the exact value and how easy it is to attribute cause. This paper reviews how directional drilling practice has evolved over 100 years, and how the wellbore quality that results from the directional drilling process can be a driver for the overall value of the well. Specifically, it draws on a number of key references to examine how tortuosity doesn't just have an influence on drilling but also how it can adversely impact completions, reliability of production equipment and even production rates. The paper proposes that we consider the whole-life value of the well as a key performance indicator as we drill. It emphasises that we must cease to focus solely on rate of penetration and the depth-time curve. The paper shows, with examples, how modern directional drilling systems can address tortuosity and improve wellbore quality. It presents an unbiased view of the industry from an independent viewpoint, exploring how directional drilling has been partially automated over the years and examining the state of the art in current automated directional drilling systems. It proposes the need for a modern directional drilling system not just in terms of drilling parameters but also in terms of automation of geometric and, ultimately, geologic aspects of directional drilling. The paper is intended to break down the silos that can exist between drilling, completions and production functions, and to help the industry to think about the long-term consequences of performance when specifying future directional drilling equipment.

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Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System

10.2118/202191-ms ◽

2021 ◽

Author(s):

Kingsley Williams Amadi ◽

Ibiye Iyalla ◽

Yang Liu ◽

Mortadha Alsaba ◽

Durdica Kuten

Keyword(s):

Real Time ◽

Specific Energy ◽

Penetration Rate ◽

Drilling Process ◽

Stick Slip ◽

Conservation Of Energy ◽

Rate Of Penetration ◽

Mechanical Specific Energy ◽

Drilling Parameters ◽

Drilling System

Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :

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Experimental Study on Drilling Process of CFRP Composite Laminate

Materials Science Forum ◽

10.4028/www.scientific.net/msf.638-642.927 ◽

2010 ◽

Vol 638-642 ◽

pp. 927-932 ◽

Cited By ~ 4

Author(s):

M.A. Azmir ◽

Praveena Nair Sivasankaran ◽

Z. Hamedon

Keyword(s):

Feed Rate ◽

Thrust Force ◽

Optimization Method ◽

Spindle Speed ◽

Drilling Process ◽

Optimal Parameters ◽

Reinforced Plastics ◽

Drilling Parameters ◽

Cfrp Composite ◽

Split Point

This thesis deals with carbon fiber reinforced plastics (CFRP) composites, an advanced material which is widely used in manufacturing aircrafts because of their unique mechanical and physical properties. The research mainly involved drilling of CFRP. This study is focused on analyzing the thrust force and delamination against drilling parameters namely feed rate, spindle speed and type of tool materials. Also, the optimal parameters were chosen using an optimization method called D optimal. It was observed that the higher the feed rate and spindle speed employed, the higher the thrust force and delamination occur. The split point fibre (SPF) drill gave the lowest values of thrust force and delamination. Based on the optimal parameters, a verification test was conducted and the prediction error was 2.3% and 5.6% for thrust force and delamination respectively. This shows, that the optimal parameters obtained is reliable as it could improve the process considerably. The results of this study could be used as a reference for further research and studies on drilling of CFRP.

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