Rate Of Penetration Model Research Articles

Data-driven recurrent neural network model to predict the rate of penetration

The Rate of Penetration (ROP) is a vital parameter in drilling operations. Due to the complex relationship between the parameters affecting ROP, accurate prediction of ROP is hard to be obtained analytically. In this study, a recurrent neural network model was developed to estimate ROP using Plastic Viscosity (PV), Mud Weight (MW), flow rate (Q), Yield Point (YP), Revolutions per Minute (RPM), Weight on Bit (WOB), nozzles total flow area (TFA), and Uniaxial Compressive Strength (UCS). The data were collected from more than 2000 wells drilled worldwide. The network architecture was optimized by trial and error. The data were categorized into three sets; 70 % for training, 15 % for validation, and 15% for testing. The created network predicted ROP with an average R2 of 0.94. With this tangible prediction method, oil and gas companies can better estimate the time of well delivery as well as optimizing ROP by altering the controllable input parameters affecting the ROP model. Artificial intelligent methods have shown their potential in solving complex problems. The oil and gas industry can benefit from artificial intelligence, especially with the large data sets available, to better optimize the drilling process.

Development of a modified Bourgoyne and Young model for predicting drilling rate

Drilling in carbonate rock is quite challenging as the formation is permeable and porous, hence, leads to the possibility of having mud losses. Since drilling into loss formation requires controlling some of the operational drilling parameters, the rate of penetration (ROP) prediction in this area, especially in carbonate formation could be challenging. As a result, the study aims to improve the accuracy of ROP prediction in carbonate loss zones by adjusting the Bourgoyne and Young (B&Y) model, which is the most comprehensive model to date and has been used until now but is missing this feature. Modification of the drilling rate model is performed based on loss severity levels by including cutting transport ratio (R T ) parameter and eliminating multicollinearity parameters. The considered elements in the modified model are loss severity level, weight-on-Bit (WOB), rotary speed (rpm), bit wear and modified hydraulics with cutting transport ratio. The North Kuwait field's drilling data was used for multiple regression analysis (MRA) with categorical coding (1,0) and generated the model. A total of 80 drilling datasets were used for model development and another 38 datasets for model verification. The modified model could predict the ROP for different loss severity levels in carbonate formation based on the results. The original model yielded an R 2 value of 0.52, while the modified model yielded an R 2 value of 0.81 for the first set of data, resulting in an ROP accuracy improvement of about 29%. Meanwhile, the original and modified models yielded R 2 values of 0.57 and 0.88, respectively, which improved the model accuracy by 31% for the second set of data. The modified model accurately predicted the ROP with a mean absolute error (MAE) of 3.21%, 4.08% and 3.50% for seepage loss, partial loss and severe loss, respectively and outperformed all the existing four ROP models. Also, this modification reduced the number of constants used for the regression analysis. Consequently, a reduction in the number of required data points and elimination of the regression process's multicollinearity effect was attained. • Application of statistical tool for drilling rate prediction in carbonate rock drilling. • Modifying traditional drilling rate model by incorporating cutting transport ratio parameter. • Effects of multicollinearity and linear dependency during regression are eliminated. • A new regression method for drilling rate prediction is proposed according to loss severity level.

Drill bits optimization using offset wells analysis and ROP modelling

Drill Bits optimization is the key factor to improve the overall drilling efficiency and therefore, reduce the cost and the non-productive time. For this, analysis of the offset field data, modelling before drilling and optimization the drilling parameters are highly required to enhance the drilling efficiency. In this paper, 8 ½” hole of three drilled wells is analysed for the rate of penetration (ROP), drilling cost per meter and the drilling duration of each drill bit. The analysis has shown that the variation in drilling efficiency was due to some factor such as, the type and features of the used drill bit, the bottom hole assembly type and the drilling parameters. ROP model (multiple regression equation) is developed by using a reference well’s drilling parameters. The drilling parameters that have been used to develop the ROP model are weight on bit (WOB), revolution per minute (RPM) of the drill string, the drilling torque (TRQ), jet impact force (JIF) of the drill bit and the unconfined compressive strength (UCS) of the formation. Parametric sensitivity study has been carried out using this model to interpret the drilling parameters that have the greatest impact on ROP. Finally, drill bits’ with the highest performance are recommended to be used based on analysis of the offset wells and ROP model “multiple regression equation” has been approached to predict the ROP values before drilling to be used in optimization of drill bits’ performance in the forthcoming drilled wells.

Estimation of Rate of Penetration Considering Mechanical, Hydraulic, and Formation Characteristics for Mishref Formation

This paper presents a detailed formulation of a rate of penetration (ROP) model, consideringmany drilling parameters and conditions for obtaining maximum drilling rate as well asminimizing the drilling cost.A regression analysis technique has been usedfor ROP modeling in Mishref formation.The datawere extracted from routinely available mud and wirelinelogs. These data includes weight onbit ,rotary speed,horse per square inch,and transit time.For ROP modeling, data of five wellsinHalfaya oil field in south Iraq were extracted.Statistical software called SPSS was used forimproving the modeling data and to perform linear and nonlinear multiple regression analysis.This improving approach included detection the outliers of modeling parameters, grouping themodeling data, moving average and finally applying the regression analysis.Results of modeling showed that the grouping of modeling data exhibited good convergencewith actual data and the overall model of oil field could produce good fitness with the actualdata in both cases of linear and nonlinear models.Also,a good estimation of drilling cost couldbe obtained when using this model.

Prediction of penetration rate in drilling operations: a comparative study of three neural network forecast methods

Optimizing purposes of the drilling process include reduction in time, saving costs, and increasing efficiency, which requires optimization of controllable variables and variables affecting the drilling process. Drilling optimization is directly related to maximizing the rate of penetration (ROP). However, estimation of ROP is difficult due to the complexity of the relationship between the variables affecting the drilling process. The main goal of this study is to develop three computational intelligence (CI)-based models including multilayer perceptron neural network optimized by backpropagation algorithm (BP-MLPNN), cascade-forward neural network optimized by backpropagation algorithm, and radial basis function neural network optimized by biogeography-based optimization algorithm (BBO-RBFNN) to estimate ROP. Also, in order to broaden the comparisons, some conventional ROP models from the literature were employed. The required data were collected from the well log unit and the final drilling reports of four drilled wells in two different oil fields in southwestern Iran. Firstly, all data were preprocessed to remove outliers; then the overall noises of the data were reduced by implementing Savitzky–Golay smoothing filter. In the next stage, nine input variables were selected during a feature selection step by combining the BP-MLPNN and NSGA-II algorithm. The results of this study showed that developed CI-based models more accurate than conventional ROP models. Also, a survey of statistical indices and graphical error tools proved that BBO-RBFNN model has the highest performance to predict ROP with values of APRE, AAPRE, RMSE and R2 equal to − 0.603, 5.531, 0.490 and 0.948, respectively.

Prediction of Penetration Rate for PDC Bits Using Indices of Rock Drillability, Cuttings Removal, and Bit Wear

SummaryPredicting rate of penetration (ROP) has gained considerable interest in the drilling industry because it is the most-effective way to improve the efficiency of drilling and reduce the operating costs. One way to enhance the drilling performance is to optimize the drilling parameters using real-time data. The optimization of the drilling parameters stands on the fact that drilling parameters are interrelated; that is, corrections in one factor affect all the others, positively or negatively.Analysis of the available models in the literature showed that they did not take into account all factors, and therefore, they might underestimate the ROP. To improve the accuracy of predicting the bit efficiency, a new ROP model is developed to preplan and lower the drilling costs. This approach introduces three parts of the process that were developed to describe the challenge of predicting ROP: aggressiveness or drillability, hole cleaning, and cutters wear, which are interrelated to each other. The approach discusses each process individually, and then the influence of all three factors on ROP is assessed. Taking into account the drilling parameters and formation properties, ROP1 is estimated by use of a new equation. Then, lifting the produced cutting to the surface and evaluating how that affects the bit performance is proposed in the second part of the process (hole cleaning). Finally, wear index is introduced in the third part (wear condition) to predict the reduction of ROP2 caused by cutter/rock friction.The approach serves and could be considered as a baseline to identify all factors that can affect the bit performance. The developed model equations are applied to estimate ROP in three vertical oil wells with different bit sizes and lithology descriptions in Libya. The results indicate that the driven model provides an effective tool to predict the bit performance. The results are found in good agreement with the actual ROP values and achieve an enhancement of approximately 40% as compared to the previous models.

Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm

Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper focused mainly on two aspects. The first one was the investigation of the whale optimization algorithm (WOA) to optimize the weights and biases between input and hidden layers of ELM to enhance its prediction accuracy. The other was to adopt a prediction methodology that seeks to update the predictive model at each formation in order to reduce the dimension of input data and mitigate the effect of non real-time data such as the formation properties on the bit speed prediction. The prediction models were trained and tested using 3561 data points gathered from an Algerian field. The statistical and graphical evaluation criteria show that the ELM-WOA exhibited higher accuracy and generalization performance compared with the ELM-PSO and MLP-LMA. Furthermore, ELM-WOA was compared with two well-known ROP correlations in the literature, and the comparison results reveal that the proposed ELM-WOA model is superior to the pre-existing correlations. The findings of this study can help for the achievement of an optimum ROP and the reduction of the non-productive time. In addition, the outputs of this study can be used as an objective function during the real-time optimization of the drilling operation.

Coupling rate of penetration and mechanical specific energy to Improve the efficiency of drilling gas wells

Drilling operations for oil or gas wells are very expensive. Optimizing the drilling efficiency and increasing the rate of penetration (ROP) will reduce the overall cost of the drilling operation. Different approaches are utilized to increase the drilling performance including analytical and empirical methods. However, most of the available models have some limitations once they applied for real-time drilling operations. This study presents a new approach for evaluating and improving the drilling operations for real-time applications. Seven ROP models were developed using artificial neural network (ANN) technique. The ANN models were developed using real field data (20,000 data sets) which includes the real-time recording of the surface drilling parameters such as rate of penetration (ROP), weight on bit (WOB), torque, rotation speed (RPM) and mud flow rate (Q). The ANN-based models are able to provide an accurate estimation for the full ROP profile. Thereafter, the predicted ROP was coupled with the calculated MSE in order to optimize the drilling performance. Moreover, and for the first time, a new (ROP/MSE) ratio is suggested, which can be used to assess the drilling operations in a real-time. A new profile of ROP/MSE ratio can be displayed along with the drilling parameter in order to provide a quick and more reliable evaluation for the ongoing drilling operation. The suggested ratio incorporates the impacts of the primary drilling factors which are the drilling speed (ROP) and the required drilling energy (MSE). Also, the seven ANN-based models, developed in this work, can predict the full profile of ROP with an average absolute percentage error (AAPE) of around 7.9% and a correlation coefficient of around 0.92.

A Semiempirical Model for Rate of Penetration with Application to an Offshore Gas Field

SummaryIn this paper, we present an accurate semiempirical rate of penetration (ROP) predictive model for polycrystalline diamond compact (PDC) bits. Our model is inspired by the model of Bourgoyne and Young (B&Y) and follows an exponential form with 10 different drilling functions to account for various factors affecting ROP in drilling operations. We extend the B&Y model to the PDC bits and discuss that a different predictive model should be obtained for each formation. On top of the factors included in the original B&Y model, our model accounts for parameters such as downhole motor, equivalent circulating density, mechanical weight on bit (WOB), and wellbore inclination. In particular, we incorporate the effect of equilibrium cuttings bed thickness and downhole cuttings concentration in the ROP model. The parameters of the model are obtained using multiple regression analysis with the field data. 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. The model can be incorporated in a framework to obtain an optimal well plan for a new well or for prescribing optimal operational parameters for well planning and real-time drilling operations. The prediction performance of the proposed model is also evaluated in various formations for several test wells across an offshore gas field. Our results indicate that the proposed model is able to predict the drilling ROP with an accuracy of more than 90%.

A New Hybrid Analytical-Machine Learning Method for Real-Time ROP Modeling

Real-time drilling optimization refers to operations and equipment that could minimize total drilling costs. Drilling speed that is called the rate of penetration (ROP) in the drilling industry can be used as a good indicator for the performance evaluation of the drilling operation. Real-time control for drilling ROP is limited to just a few controllable parameters during drilling operations, that is, WOB, RPM, and hydraulics. These parameters can be controlled from the surface by the driller in real-time. In the traditional methods of ROP modeling, an inflexible equation could be developed between some important effective drilling parameters such as weight on the bit or bit rotational speed and drilling rate of penetration. These models had a low degree of accuracy, and they were not applicable in the newly drilled wells even in the same field with an acceptable degree of accuracy. In this study, a new real-time continues-learning method for ROP modeling was developed. In this method, as the drilling operation gets starts and the drilling data reaches the surface, ROP modeling starts, and as the drilling continues, the model accuracy increases. For the method evaluation, 5 famous existing analytical drilling model was selected. Also, a new ROP model was developed in this work. All of these 6 models contain some constant coefficients that were obtained using a new machine learning method named Rain Optimization Algorithm. In the end, the accuracy of the models was compared. Results show that the presented method for ROP modeling is a very flexible method with a high degree of accuracy that can be easily used in any formation. Also, the newly presented model could increase the accuracy of ROP prediction from 75% to 81%.

Evaluating the maximum rate of penetration for drilling borehole in soft coal seam

AbstractMost coal seams in China are characterized by high‐gas pressure and high‐gas content, which seriously threaten the safety of coal mines. Gas extraction is an effective measure for preventing gas disasters. However, the drilling process in soft coal seams is inefficient; it is difficult for the boreholes to reach the design length. The rate of penetration (ROP) plays an important role in determining drilling efficiency and borehole length in soft coal seams. In this study, we establish a simplified ROP model for soft coal seams and evaluate the maximum ROP of Hebi No. 6 coal mine. First, the drill cutting transport capacity of the drilling system and drill cutting quantity generated during the drilling process are analyzed. By simplifying drill cutting transport and coal seam properties, the ROP model is established. Second, the Rock Failure Process Analysis software is used to analyze the deformation region of boreholes, which has a great influence on the drill cutting transport space. Finally, a field test is implemented in Hebi No. 6 coal mine in Henan Province, China, to verify the model, and we propose a measure to control the ROP below the maximum value to increase the length of the borehole. The test results indicate that the model has high accuracy, with errors less than 15%, and the lengths of boreholes in air‐return and transportation roadways are increased to 60 and 80 m, respectively, and the increase rate is greater than 50%.

Developing a new rigorous drilling rate prediction model using a machine learning technique

Drilling rate of penetration (ROP) prediction is an enormously important step to optimize drilling controllable parameters. Therefore, numerous efforts have been done in order to present a more precise estimator model that is still ongoing. The results of the literature review indicated that, between the two approaches followed for ROP modeling, namely physic-based models and data-driven methods, the use of the data-driven methods has grown significantly. The literature review made two points clear: (1) the geomechanical parameters were not considered adequately, and (2) no previous research had applied a combination of least-squares support-vector machines (LSSVM) with cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithms (GA). This led us to use hybrid algorithms for ROP modeling at vertical wells drilled in the southwestern Iran. For this purpose, mud logging parameters (including depth (Depth), mud density (MD), torque (Tor), standpipe pressure (SPP), equivalent circulating density (ECD), weight on bit (WOB), revolutions per minute (RPM), flow rate (FR), and ROP) and geomechanical parameters (including gamma ray (GR), porosity (NPHI), density (RHOB), and uniaxial compressive strength (UCS)) were collected along the studied wells. Histogram analysis and pairwise evaluation of the studied features indicated the presence of outliers among the data points. Accordingly, the Tukey's method was used to omit the outliers. Subsequently, the most relevant features in the ROP prediction were selected using a combination of the non-dominated sorting genetic algorithm II (NSGA-II) with multilayer perceptron (MLP) neural network. The results showed that one could attenuate the modeling error by increasing the number of input parameters into the ROP estimation model. However, the improvement was very subtle when the number of input parameters exceeded six. Therefore, six parameters (UCS, FR, WOB, Depth, MD, and RPM) were used for ROP modeling using LSSVM-COA, LSSVM-PSO, and LSSVM-GA hybrid algorithms. Results of applying these hybrid algorithms indicated that the LSSVM-COA outperformed the other two algorithms in terms of accuracy and the coefficient of determination. In a next step, the LSSVM-COA was trained on both the outlier-omitted and raw datasets, indicating the important role of the outlier omission step in achieving a highly accurate and reliable model. Continuing with the work, in order to evaluate the performance of the LSSVM-COA hybrid model, the model was compared to support-vector regression (SVR)-COA, MLP-COA, Motahhari's, linear multivariate regression (LMR), and nonlinear multivariate regression (NLMR) models. The results confirmed superior accuracy of the LSSVM-COA model over the examined models. The small difference between the obtained levels of error in the training and testing stages with the LSSVM-COA model, as compared to the other models, revealed that the model can be used to predict the ROP at other wells across the field reliably and accurately provided the model be developed with larger sets of data across the field. Predicting ROP with high accuracy at the test Well clearly proved this claim.

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

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.

Enhancing Reamer Drilling Performance in Deepwater Gulf of Mexico Wells

SummaryReamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology.Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning.Separate ROP models are trained for reamer-controlled and bit-controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit-reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well.The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.

New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

Rate of penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the petroleum industry, most of the well cost is taken by the drilling operations. Therefore, it is very crucial to drill carefully and improve drilling processes. Nevertheless, it is challenging to predict the influence of every single parameter because most of the drilling parameters depend on each other and altering an individual parameter will have an impact on the rest. Due to the complexity of the drilling operations, up to the present time, there is no reliable model that can adequately estimate the ROP. Artificial intelligence (AI) might be capable of building a predictive model from a number of input parameters that correlate to the output parameter. A real field dataset, of shale formation, that contains records of both drilling parameters such as, rotation per minute (RPM), weight on bit (WOB), drilling torque (τ), standpipe pressure (SPP) and flow pump (Q) and mud properties such as, mud weight (MW), funnel and plastic viscosities (FV) (PV), solid (%) and yield point (YP) were used to predict ROP using artificial neural network (ANN). A comparison between the developed ANN-ROP model and the number of selected published ROP models were performed. A novel empirical equation of ROP using the above-mentioned parameters was derived based on ANN technique which is able to estimate ROP with excellent precision (correlation coefficient (R) of 0.996 and average absolute percentage error (AAPE) of 5.776%). The novel ANN-based correlation outperformed three published empirical models and it can be used to predict the ROP without the need for artificial intelligence software.

A New Hybrid Bat Algorithm and its Application to the ROP Optimization in Drilling Processes

Rate of penetration (ROP) optimization is crucial for drilling processes due to its vital role in increasing efficiency. This article proposes a new hybrid bat algorithm (HBA) to achieve the maximum ROP accurately. A data-driven ROP model is established by combining the wavelet filtering and optimized support vector regression according to the drilling characteristics. After that, five modifications, namely, iterative local search, stochastic inertia weight, pulse rate and loudness improvement, directional echolocation, and modified local random walk are combined to further improve the global optimization performance of the bat algorithm. Extensive experiment results on the IEEE Congress on Evolutionary Computation 2005 standard benchmark problems show that the proposed algorithm has successful outcomes compared with ten conventional algorithms. Additionally, in the application of the HBA to a real-world drilling process in Shennongjia area, Central China, the ROP has been improved by 34.84%, which is the largest compared with the conventional popular ROP optimization methods.

Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

Drilling wells in challenging oil/gas environments implies in large capital expenditure on wellbore's construction. In order to optimize the drilling related operation, real-time decisions making have been put in place, so that prediction of rate of penetration (ROP) with accuracy is essential. Despite many efforts (theoretical and experimental) throughout the years, modeling the ROP as a mathematical function of some key variables is not so trivial, due to the highly non-linearity behavior experienced. Therefore, several researches in the recent years have been proposing to use data-driven models from artificial intelligence field for ROP prediction and optimization.This paper presents an extensive review of the literature on ROP prediction, especially, with machine learning techniques, as well as how these models can be used to optimize the drilling activities. The ROP models are classified as traditional models (based on physics-models), statistical models (e.g. multiple regression), or machine learning methods. This review enables to see that machine learning techniques can potentially outperform in terms of ROP-prediction accuracy on top of traditional or statistical models. Throughout this work, an extensive analysis of different ways of obtaining ROP models is carried out, concluding with different strategies adopted in literature to perform data-driven model optimization.Despite the saving potential which can be achieved with real-time optimization based on data-driven ROP models, it is noticeable that there is a lack of implementation of those techniques in the industry, as per literature review. To take a step forward in real implementations, the petroleum industry must be aware that yet no rule of thumb already exists on this specific area, but still, good and very reasonable results can be achieved by following the best practices identified in this review. In addition, the modern practices of machine learning provide promising guidelines for implementing projects in oil and gas industry.

Rate of penetration (ROP) optimization in drilling with vibration control

Drilling optimization is typically tackled by optimizing the rate of penetration (ROP). However, most ROP optimization models do not consider the effect of drilling vibrations, a major ROP inhibitor. To resolve this limitation, this paper introduces a workflow that combines the ROP optimization process with a machine learning-based vibration model. This model determines optimal drilling parameters that not only increase ROP but mitigate excessive vibrations. Analytical ROP models are used to model the ROP in a given formation. An optimization algorithm (gradient ascent with random restarts) is used to find the optimal drilling control parameters, weight on bit (WOB) and revolutions per minute (RPM), required to improve ROP ahead of the bit. The vibrations classification model is used as an equality constraint for the optimization algorithm, to set bounds on the optimization space, ensuring that the selected optimal parameters do not result in excessive vibrations when implemented. The model, when evaluated on field data, shows an improvement of ROP by 14.1% (10 ft/h) on average across all formations when compared to the measured data. A case study has been discussed to illustrate the use of this approach in drilling practice. This model introduces a novel way to couple drilling ROP models with vibration models to improve ROP and to mitigate vibrations.

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.

Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network

The rate of penetration (ROP) is one of the key factors that affect the drilling costs. Optimizing the ROP is a big challenge as it depends on many factors such as revolutions per minute (RPM), weight on bit (WOB), torque (T), horsepower (HP), and uniaxial compressive strength (UCS) of the drilled rocks. In addition, drilling fluid properties have a major effect on ROP. The main goal of this study is to develop a new ROP model using an artificial neural network (ANN) combined with the self-adaptive differential evaluation (SaDE) technique. The model was built using different drilling mechanical parameters and drilling fluid properties. A new ROP empirical correlation was developed by extracting the weights and biases of the optimized SaDE-ANN model. The optimized ANN architecture based on SaDE is 5-30-1, where five input parameters were used in the input layers to predict the ROP which are drilling fluid density to plastic viscosity ratio, RPM, WOB/D, T/UCS, and HP. The optimized number of neurons was 30 and the output layer consists of one output parameter which is ROP. The data was divided into 60% training and 40% testing. The developed ROP model based on SaDE-ANN showed high accuracy where the correlation coefficient (R) was 0.98 and the average absolute percentage error (AAPE) was 5%. The new ROP empirical correlation outperformed the previous ROP models.