A Robust Rate of Penetration Model for Carbonate Formation

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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Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Sensors ◽

10.3390/s21113592 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3592

Author(s):

Naipeng Liu ◽

Di Zhang ◽

Hui Gao ◽

Yule Hu ◽

Longchen Duan

Keyword(s):

Decision Making ◽

Rheological Properties ◽

Real Time ◽

Drilling Fluid ◽

Time Measurement ◽

Decision Making Process ◽

Rate Of Penetration ◽

The Real ◽

Real Time Measurement ◽

Fluid Properties

The accurate and frequent measurement of the drilling fluid’s rheological properties is essential for proper hydraulic management. It is also important for intelligent drilling, providing drilling fluid data to establish the optimization model of the rate of penetration. Appropriate drilling fluid properties can improve drilling efficiency and prevent accidents. However, the drilling fluid properties are mainly measured in the laboratory. This hinders the real-time optimization of drilling fluid performance and the decision-making process. If the drilling fluid’s properties cannot be detected and the decision-making process does not respond in time, the rate of penetration will slow, potentially causing accidents and serious economic losses. Therefore, it is important to measure the drilling fluid’s properties for drilling engineering in real time. This paper summarizes the real-time measurement methods for rheological properties. The main methods include the following four types: an online rotational Couette viscometer, pipe viscometer, mathematical and physical model or artificial intelligence model based on a Marsh funnel, and acoustic technology. This paper elaborates on the principle, advantages, limitations, and usage of each method. It prospects the real-time measurement of drilling fluid rheological properties and promotes the development of the real-time measurement of drilling rheological properties.

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Micro-Testing While Drilling for Rate of Penetration Optimization: Experiments and Simulations

Journal of Offshore Mechanics and Arctic Engineering ◽

10.1115/1.4053127 ◽

2021 ◽

pp. 1-49

Author(s):

Magnus Nystad ◽

Bernt Aadnoy ◽

Alexey Pavlov

Keyword(s):

Optimization Method ◽

Extremum Seeking ◽

Constraint Handling ◽

Drilling Process ◽

Test Results ◽

Rate Of Penetration ◽

Drilling Rig ◽

Model Free ◽

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 (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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A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽

10.3390/s20072058 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2058 ◽

Cited By ~ 2

Author(s):

Salaheldin Elkatatny ◽

Ahmed Al-AbdulJabbar ◽

Khaled Abdelgawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mutual Effect ◽

Percentage Error ◽

Drilling Process ◽

Ann Model ◽

Data Set ◽

Individual Parameter ◽

Rate Of Penetration ◽

Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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The effective application of mud cooler machine to support to carry out the infill wells in Cuu Long basin

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2021.62(3a).09 ◽

2021 ◽

Vol 62 (3a) ◽

pp. 76-84

Author(s):

Tuan Tran Nguyen ◽

Son Hoang Nguyen ◽

Keyword(s):

Oil And Gas ◽

Drilling Fluid ◽

Drilling Process ◽

Fluid Temperature ◽

Gas Drilling ◽

Drilling Rig ◽

Effective Application ◽

Safe Level ◽

Fluid Properties ◽

Temperature And Pressure

This paper presents some studies on the application of mud cooler in Oil and Gas drilling in a high temperature, high pressure condition of Cuu Long reservoir. The authors have proposed a method to study the theory of temperature effects on drilling fluid properties, that have been tested practically. The authors have remarked on each type of drilling rig and installation location. With these remarks, the authors give an option to install the "Mud cooler" on the rig at the appropriate location and method so that the temperature of the solution will be ensured to reduce to a safe level. The effective application of this equipment has greatly assisted drilling process since the fluid temperature has been reduced sharply before returning to the mud tank. This has helped cut down expenses significantly by prolonging eqipment's endurability, saving time for drilling, ship renting, drilling services and minimize the budget spent on buying the fluid and additives to recover it. Thus, the drilling workers' working conditions have been facilitated. The results of these studies have been proved scientifically and practically through the successful drilling of well ST-3P-ST. This will make the way for other local wells and reservoirs which have the same conditions of temperature and pressure.

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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

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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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NETL Extreme Drilling Laboratory Studies High Pressure High Temperature Drilling Phenomena

Journal of Energy Resources Technology ◽

10.1115/1.3000139 ◽

2008 ◽

Vol 130 (4) ◽

Cited By ~ 13

Author(s):

K. David Lyons ◽

Simone Honeygan ◽

Thomas Mroz

Keyword(s):

Physical Simulation ◽

Drilling Fluid ◽

Fluid Pressure ◽

Rock Properties ◽

Drilling Fluids ◽

Laboratory Studies ◽

Drilling Parameters ◽

High Pressure High Temperature ◽

Fluid Properties ◽

Weight On Bit

The U.S. Department of Energy’s National Energy Technology Laboratory (NETL) established the Extreme Drilling Laboratory to engineer effective and efficient drilling technologies viable at depths greater than 20,000 ft. This paper details the challenges of ultradeep drilling, documents reports of decreased drilling rates as a result of increasing fluid pressure and temperature, and describes NETL’s research and development activities. NETL is invested in laboratory-scale physical simulation. Its physical simulator will have capability of circulating drilling fluids at 30,000 psi and 480°F around a single drill cutter. This simulator is not yet operational; therefore, the results will be limited to the identification of leading hypotheses of drilling phenomena and NETL’s test plans to validate or refute such theories. Of particular interest to the Extreme Drilling Laboratory’s studies are the combinatorial effects of drilling fluid pressure, drilling fluid properties, rock properties, pore pressure, and drilling parameters, such as cutter rotational speed, weight on bit, and hydraulics associated with drilling fluid introduction to the rock-cutter interface. A detailed discussion of how each variable is controlled in a laboratory setting will be part of the conference paper and presentation.

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Mathematical Model of the Diamond-Bit Drilling Process and Its Practical Application

Society of Petroleum Engineers Journal ◽

10.2118/10148-pa ◽

1982 ◽

Vol 22 (06) ◽

pp. 911-922 ◽

Cited By ~ 3

Author(s):

Malgorzata B. Ziaja ◽

Stefan Miska

Keyword(s):

Penetration Rate ◽

Drilling Fluid ◽

Active Surface ◽

Rock Properties ◽

Depth Of Cut ◽

Drilling Process ◽

Diamond Cutting ◽

Rate Of Penetration ◽

Drilling Performance ◽

Diamond Bit

Abstract With several limiting assumptions, a mathematical model of the diamond-bit drilling, process has been developed. The model represented by an instantaneous rate-of-penetration equation takes into account the reduction in penetration rate during drilling resulting from bit wear. The model has been tested both under laboratory and under field conditions. The comparison of the theoretical and experimental results has shown reasonable agreement. A method for estimating rock properties also has been established. Using this method, we can find the so-called index of rock strength and the index of rock abrasiveness. Introduction Several published studies concerned with diamond-bit drilling report on rock properties and drillability. drilling fluid additives, diamond wear, and drilling performance theories. Among the factors, that affect diamond-bit drilling performance, the type of formation to be drilled is of utmost importance since it significantly affects the type of bit, the drilling practices. and subsequently the rate of penetration and the drilling cost. The nature of the formation is also one of the main factors in planning deep wells, fracture jobs, mud and cement technologies, etc. For rock properties evaluation as well as for selection of proper drilling practices, several descriptions of the diamond-bit drilling process have been developed. The relevant literature is extensive and is not reviewed in this paper. The objective of this paper is to describe the diamondbit drilling model for surface-set diamond core bits and its application to determining the index of formation strength and the index of formation abrasiveness. The main difference between our model and the models known in literature is that we consider the effect of friction between the diamond cutting surfaces and the rock. A decrease in penetration rate is observed if the drilling parameters, are constant and if the formation is macroscopohomogeneous. Drilling Model The drilling model for a surface-set diamond core bit is subjected to the following limiting assumptions.Rock behavior during cutting with a single diamond may be approximated by a rigid Coulomb plastic material.The active surface of the bit is flat, and diamonds are spherical with diameter. d.The cross-sectional area of the chip formed by a single diamond is equal to the diamond cutting surface and can be established by geometry.During drilling, the neighboring diamonds work together to make a uniform depth of cut (Fig. 1).A number of diamonds forming one equivalent blade have to provide it uniform depth of cut from the inner to the outer diameter of the diamond core bit. so the bit is modeled to be a combination of several equivalent blades (Fig. 2).The diamond distribution technique provides uniform radial coverage that results in equally loaded cutting diamonds.Individual cutting diamonds perform some work that results from the friction between the rock and the diamond.Bit wear is assumed to be gradual while drilling is in progress. Under the preceding assumptions we may state that the drilling rate of the surface-set diamond core bit is a function only of weight on bit (WOB), rotary speed, average density of the diamonds on the bit's active surface, diamond size, core-bit diameters, rock properties, and degree of diamond dullness. The effects of flow rate, differential pressure, hydraulic lift, drilling fluid properties. and drillstring dynamics are ignored. According to Peterson, the penetration rate of the diamond bit, after some modifications, can be described by the following simplified equation. (1) This equation does not include the effect of diamond wear and hence pertains to unworn bits or to when bit dullness is negligible. SPEJ P. 911^

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Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

Materials ◽

10.3390/ma14081983 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1983

Author(s):

Mahmood Ahmad ◽

Ji-Lei Hu ◽

Feezan Ahmad ◽

Xiao-Wei Tang ◽

Maaz Amjad ◽

...

Keyword(s):

Compressive Strength ◽

High Temperature ◽

Supervised Learning ◽

Mean Squared Error ◽

Cement Content ◽

Coefficient Of Determination ◽

Percentage Error ◽

Learning Methods ◽

Inference System ◽

Compressive Strength Of Concrete

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

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Inline Drilling Fluid Property Measurement, Integration, and Modeling to Enhance Drilling Practice and Support Drilling Automation

10.2118/208064-ms ◽

2021 ◽

Author(s):

Sanjit Roy ◽

Saiyid Z. Kamal ◽

Richard Frazier ◽

Ross Bruns ◽

Yahia Ait Hamlat

Keyword(s):

Real Time ◽

Drilling Fluid ◽

Measurement Data ◽

Lessons Learned ◽

Valuable Insight ◽

Drilling Process ◽

Time Data ◽

Fluid Property ◽

Real Time Data ◽

Fluid Properties

Abstract Frequent, reliable, and repeatable measurements are key to the evolution of digitization of drilling information and drilling automation. While advances have been made in automating the drilling process and the use of sophisticated engineering models, machine learning techniques to optimize the process, and lack of real-time data on drilling fluid properties has long been recognized as a limiting factor. Drilling fluids play a significant function in ensuring quality well construction and completion, and in-time measurements of relevant fluid properties are key to automation and enhancing decision making that directly impacts well operations. This paper discusses the development and application of a suite of automated fluid measurement devices that collect key fluid properties used to monitor fluid performance and drive engineering analyses without human involvement. The deployed skid-mounted devices continually and reliably measure properties such as mud weight, apparent viscosity, rheology profiles, temperatures, and emulsion stability to provide valuable insight on the current state of the fluid. Real-time data is shared with relevant rig and office- based personnel to enable process monitoring and trigger operational changes. It feeds into real-time engineering analyses tools and models to monitor performance and provides instantaneous feedback on downhole fluid behavior and impact on drilling performance based on current drilling and drilling fluid property data. Equipment reliability has been documented and demonstrated on over 30 wells and more than 400 thousand ft of lateral sections in unconventional shale drilling in the US. We will share our experience with measurement, data quality and reliability. We will also share aspects of integrating various data components at disparate time intervals into real-time engineering analyses to show how real-time measurements improve the prediction of well and wellbore integrity in ongoing drilling operations. In addition, we will discuss lessons learned from our experience, further enhancements to broaden the scope, and the integration with operators, service companies and other original equipment manufacturer in the domain to support and enhance the digital drilling ecosystem.

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