Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

Abbas, Ahmed K.; Rushdi, Salih; Alsaba, Mortadha; Dushaishi, Mohammed Al

doi:10.1115/1.4043699

Cited by 45 publications

(7 citation statements)

References 50 publications

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“… 64 − 67 The tool has the capability to mimic the biological neural system for thinking by problem learning. 68 , 69 The structure of the ANN tool starts from a minimum of three layers named the input layer for the input parameters, hidden layer for processing, and output layer for the target parameter prediction. 70 − 73 The tool has interconnected neurons for linking the layers and affects the performance of the network processing.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud

et al. 2022

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The drilling fluid rheology is a critical parameter during the oil and gas drilling operation to achieve optimum drilling performance without nonproductive time or extra remedial operation cost. The close monitoring for rheological properties will help the drilling fluid crew to take quick actions to maintain the designed profiles for the drilling fluid rheology, especially when it comes to the flat rheology drilling fluid system, which is a new generation for harsh and specific drilling conditions that require flat profiles for the mud rheology regarding the temperature condition changes. The current study introduces a machine learning application toward predicting the rheology of synthetic oil-based mud (flat rheology type) for the full automation system of monitoring the mud rheological properties. Four models are developed, for the first time, to determine the rheological characteristics of flat rheology synthetic oil-based system using artificial neural networks. The developed models are capable of predicting the plastic and apparent viscosities, yield point, and flow behavior index from only the mud density and Marsh funnel as model inputs. The proposed models were trained and optimized from a real field dataset (369 measurements) with further testing the models using an unseen dataset of 153 data points. The predicted rheological properties achieved a high degree of accuracy versus the actual measurements and showed a coefficient of correlation range from 0.91 to 0.97 with an average absolute percentage error of less than 9.66% during the training and testing phases. Besides, machine learning-based correlations are proposed for estimating the rheological properties on the rig site without running the machine learning system for easy field applications.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud

et al. 2022

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“…An ANN tool was utilized for solving engineering problems by its processing algorithms based on interconnected artificial neurons that mimic the biological neural networks. , Three layers represented the common architecture for ANN, which are the input, hidden, and output layers . Weights and biases are utilized in the ANN structure to link the layers and affect the network performance .…”

Section: Materials and Methodsmentioning

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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“…ANN tool was utilized for solving engineering problems by its processing algorithms based on interconnected artificial neurons that mimic the biological neural networks 54,55 . Three layers represented the common architecture for ANN which are the input layer, hidden layer, and output layer 56 .…”

Section: Artificial Neural Network (Ann) Modelmentioning

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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Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

Cited by 45 publications

References 50 publications

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

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

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