A New Method for Calculating the Equivalent Circulating Density of Drilling Fluid in Deepwater Drilling for Oil and Gas

Zhang, Hui; Sun, Tiezhi; Gao, Deli; Tang, Haixiong

doi:10.1007/s10553-013-0466-0

Cited by 9 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from annular pressure losses and cuttings concentration in the drilling uid, other factors affecting ECD during drilling operations are wellbore geometry, mud properties such as density and viscosity, mud pumping rate, and downhole pressure and temperature [3][4][5][6] . These variables collectively play crucial roles in determining the ECD levels encountered during drilling activities, necessitating careful monitoring and management to ensure operational e ciency and well integrity to prevent adverse, well-control incidents.…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine-Learning-Based Prediction of Equivalent Circulating Density Using Surface-based Drilling Data

Ekechukwu,

Adejumo

2024

Preprint

View full text Add to dashboard Cite

When drilling wells for energy explorations, it is important to regulate the formation pressures appropriately to prevent kicks, which can lead to unimaginable loss of lives and properties. This is usually done by controlling the equivalent circulating density (ECD), which responds to the dynamic conditions that occur during drilling. The conventional approach to determine ECD is via mathematical modeling or downhole measurements. However, the downhole measurement tools can be very expensive, and the mathematical models do not provide a high degree of accuracy. Some previous authors have proposed using machine learning (ML) techniques to improve the degree of accuracy of the ECD predictions. In this work, we employed an extreme gradient-boosting (XGBoost) methodology to predict ECD values. The model's accuracy was determined using correlation coefficients (R2) and root mean square errors (RMSE) as their performance metrics. The results showed a strong prediction capability with an R2 and RMSE of 1.00 and 0.0005 for the training data and an R2 and RMSE of 0.989 and 0.023 for the testing/blind data set, respectively. Lastly, we used SHAP (SHapley Additive exPlanations) approach, a concept borrowed from cooperative game theory, to enhance the interpretability and the explainability of the model prediction values.

show abstract

Section: Introductionmentioning

confidence: 99%

Explainable Machine-Learning-Based Prediction of Equivalent Circulating Density Using Surface-based Drilling Data

Ekechukwu,

Adejumo

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…During the drilling operations, several factors were found to have an impact on the ECD, and among them were the annular pressure losses, wellbore geometry, mud properties (density and viscosity), mud pumping rate, downhole pressure and temperature, and concentration of cuttings. − …”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

2021

View full text Add to dashboard Cite

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.

show abstract

“…During the drilling operations, several factors were found to have an impact on the ECD, and among them, the annular pressure losses, wellbore geometry, mud properties (density and viscosity), mud pumping rate, downhole pressure and temperature, and concentration of cuttings [2][3][4][5] .…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

A New Method for Calculating the Equivalent Circulating Density of Drilling Fluid in Deepwater Drilling for Oil and Gas

Cited by 9 publications

References 5 publications

Explainable Machine-Learning-Based Prediction of Equivalent Circulating Density Using Surface-based Drilling Data

Explainable Machine-Learning-Based Prediction of Equivalent Circulating Density Using Surface-based Drilling Data

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

Contact Info

Product

Resources

About