Application of artificial neural network to predict the rate of penetration for S-shape well profile

Al-AbdulJabbar, Ahmad; Gamal, Hany; Elkatatny, Salaheldin

doi:10.1007/s12517-020-05821-w

Cited by 35 publications

(9 citation statements)

References 29 publications

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“…In the oil and gas industry, many studies utilized ML techniques for finding solutions for practical challenges. − Intelligent models were accomplished by AI tools for many purposes such as identifying the formation lithology, predicting the formation and fracture pressures, estimating the properties of reservoir fluids, estimating the oil recovery factor, , predicting the tops of the drilled formation, rate of penetration (ROP) prediction and optimization for different drilled formations and well profiles, − determining the content of total organic carbon, − and estimating the rock static Young’s modulus, − predicting the compressional and shear sonic times, determining the rock failure parameters, detecting the downhole abnormalities during horizontal drilling, determining the wear of a drill bit from the drilling parameters, and predicting the rheological properties of drilling fluids in real time. ,− …”

Section: Predicting Ecd By Employing ML Techniquesmentioning

confidence: 99%

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

2021

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Equivalent circulating density (ECD) is considered a critical parameter during the drilling operation, as it could lead to severe problems related to the well control such as fracturing the drilled formation and circulation loss. The conventional way to determine the ECD is either by carrying out the downhole tool measurements or by using mathematical models. The downhole measurement is costly and has some limitations with the practical operations, while the mathematical models do not provide a high level of accuracy. Determination of the ECD should have a high level of accuracy, and therefore, the objective of this study is to employ machine learning techniques such as artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs) to predict the ECD from only the drilling data with a high accuracy level. The study utilized drilling data from a horizontal drilling section that includes drilling parameters (penetration rate, rotating speed, torque, weight on bit, pumping rate, and pressure of standpipe). The models were built and tested from a data set that has 3570 data points, and another data set of 1130 measurements was employed for validating the models. The accuracy of the models was determined by key performance indices, which are the coefficient of correlation (R) and the average absolute percentage error (AAPE). The results showed the strong prediction capability for ECD from the two models through training, testing, and validation processes with R greater than 0.98 and a very low error of 0.3% for the ANN model, while ANFIS recorded R of 0.96 and AAPE of 0.7, and hence, the two models showed great performance for ECD estimation application. Also, the study introduces a newly developed equation for ECD determination from drilling data in real time.

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Section: Predicting Ecd By Employing ML Techniquesmentioning

confidence: 99%

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

2021

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“…It has been widely used in petroleum engineering as it not only has the ability to solve complex problems and deal with the big data but also perfectly represents them with high accuracy compared to other models . Different models were introduced for different purposes such as ROP prediction and optimization for different drilled formations and well profiles, estimating the oil recovery factor, lithology classification, well planning, the formation lithology, prediction of formation tops, estimating the properties of reservoir fluids, fracture density estimation, detecting the downhole abnormalities during horizontal drilling, wellbore stability, predicting the compressional and shear sonic times, fracture pressure prediction while drilling, estimating the content of total organic carbon, identifying and estimating the rock failure parameters, estimating the wear of a drill bit from the drilling parameters, predicting the rheological properties of drilling fluids in real time, − and estimating the rock static Young’s modulus. , …”

Section: Machine Learning Studies In Petroleum Engineeringmentioning

confidence: 99%

Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters

2021

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Real-time prediction of the formation pressure gradient is critical mainly for drilling operations. It can enhance the quality of decisions taken and the economics of drilling operations. The pressure while drilling tool can be used to provide pressure data while drilling, but the tool cost and its availability limit its usage in many wells. The available models in the literature for pressure gradient prediction are based on well logging or a combination of some drilling parameters and well logging. The well-logging data are not available for all wells in all sections in most wells. The objective of this paper is to use support vector machines, functional networks, and random forest (RF) to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters. The used parameters are mud flow rate (Q), standpipe pressure, rate of penetration, and rotary speed (RS). A data set of 3239 field data points was used to develop the predictive models. A different data set unseen by the model was utilized for the validation of the proposed models. The three models predicted the pore pressure gradient with a correlation coefficient (R) of 0.99 and 0.97 for training and testing, respectively. The root-mean-squared error (RMSE) ranged from 0.008 to 0.021 psi/ft for training and testing, respectively, between the predicted and the actual pore pressure data. Moreover, the average absolute percentage error (AAPE) ranged from 0.97% to 3.07% for training and testing, respectively. The RF model outperformed the other models by an R of 0.99 and RMSE of 0.01. The developed models were validated using another data set. The models predicted the pore pressure gradient for the validation data set with high accuracy (R of 0.99, RMSE around 0.01, and AAPE around 1.8%). This work shows the reliability of the developed models to predict the pressure gradient from both mechanical and hydraulic drilling parameters while drilling.

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“…In the oil and gas industry, many studies utilized machine learning techniques for finding solutions for practical challenges [20][21][22][23] . Intelligent models were accomplished by artificial intelligence tools for many purposes as identifying the formation lithology 24 , predicting the formation and fracture pressures 25,26 , estimating the properties of reservoir fluids 27 , estimating the oil recovery factor 28,29 , predicting the tops of the drilled formation 30 , ROP prediction and optimization for different drilled formations and well profiles [31][32][33] , determining the content of total organic carbon [34][35][36] , and estimating the rock static Young's modulus [37][38][39][40] , predicting the compressional and shear sonic times 41 , determining the rock failure parameters 42, detecting the downhole abnormalities during horizontal drilling 43 , determining the wear of a drill bit from the drilling parameters 44 , and predicting the rheological properties of drilling fluids in real-time [45][46][47][48][49] .…”

Section: Predicting Ecd By Employing Machine Learning Techniquesmentioning

confidence: 99%

Intelligent Prediction of The Equivalent Circulating Density From Surface Data Sensors During Drilling By Employing Machine Learning Techniques

Gamal

Abdelaal

Elkatatny

2021

Preprint

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The precise control for the equivalent circulating density (ECD) will lead to evade well control issues like loss of circulation, formation fracturing, underground blowout, and surface blowout. Predicting the ECD from the drilling parameters is a new horizon in drilling engineering practices and this is because of the drawbacks of the cost of downhole ECD tools and the low accuracy of the mathematical models. Machine learning methods can offer a superior prediction accuracy over the traditional and statistical models due to the advanced computing capacity. Hence, the objective of this paper is to use the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques to develop ECD prediction models. The novel contribution for this study is predicting the downhole ECD without any need for downhole measurements but only the available surface drilling parameters. The data in this study covered the drilling data for a horizontal section with 3,570 readings for each input after data preprocessing. The data covered the mud rate, rate of penetration, drill string speed, standpipe pressure, weight on bit, and the drilling torque. The data used to build the model with a 77:23 training to testing ratio. Another data set (1,150 data points) from the same field was used for models` validation. Many sensitivity analyses were done to optimize the ANN and ANFIS model parameters. The prediction of the developed machine learning models provided a high performance and accuracy level with a correlation coefficient (R) of 0.99 for the models' training and testing data sets, and an average absolute percentage error (AAPE) less than 0.24%. The validation results showed R of 0.98 and 0.96 and AAPE of 0.30% and 0.69% for ANN and ANFIS models respectively. Besides, a mathematical correlation was developed for estimating ECD based on the inputs as a white-box model.

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Application of artificial neural network to predict the rate of penetration for S-shape well profile

Cited by 35 publications

References 29 publications

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters

Intelligent Prediction of The Equivalent Circulating Density From Surface Data Sensors During Drilling By Employing Machine Learning Techniques

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