Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids
Abstract
:1. Introduction
2. Data Collecting and Description
- ❖
- The FD has a direct impact on downhole hydrostatic pressure, which is exerted by the fluid column in the wellbore. The FD influences the FV in several ways. Higher FD increases the FV due to higher hydrostatic pressure, which pushes more fluid through the filter. Additionally, particle–fluid interactions affect cake formation and filtration rate, while changes in temperature and pressure alter the fluid’s properties, affecting its ability to filter [11,12,27,28,32].
- ❖
- The MFV measures the viscosity of DFs under realistic conditions. This test estimates the time it takes for a fixed volume of fluid to flow through a narrow orifice, which is influenced by the Reynolds number, a measure of the drag coefficient. A higher MFV indicates a thicker, more resistant fluid that flows more slowly, resulting in a lower ability to infiltrate pores, thereby resulting in a lower FV [11,28,33,34].
3. Methodology
3.1. Data Normalization and Outlier Detection
- Provide a robust and unbiased estimate of the model’s performance using distinctive groups of data records.
- Minimize overfitting risks by identifying diverse outliers.
3.2. Hybridizing ML and GO Algorithms
4. Results
4.1. Data Exploration
4.2. Data Preprocessing
4.3. Tuning the Machine Learning and Optimization Algorithms
4.4. Developing Predictive Models
5. Discussion
5.1. Overfitting Index (OFI) Assessment of the Models
5.2. Score Analysis of the Models
5.3. Robustness Analysis of the ML and the HML Models
5.4. Feature Importance Analysis
5.5. Partial Dependent Plot (PDP) Analysis
5.6. Independent Verification of Model Prediction Capability
6. The Significance of Frequent and Reliable FV Predictions While Drilling
7. Conclusions
- ❖
- A two-parameter prediction model is developed, for the first time, to accurately estimate the drilling fluids’ FV in semi-real-time.
- ❖
- A total of forty-eight data records were identified and eliminated as outliers through the GPR-MD-cross-validation technique.
- ❖
- The HML model, RBFNN-GO, exhibited the best FV prediction performance, achieving an RMSE of 0.519 mL (training subset) and 0.6396 mL (testing subset).
- ❖
- The RBFNN-GO model achieved the lowest overfitting index of 0.0327, identifying it as the most generalizable of the four models evaluated.
- ❖
- Score analysis confirmed the superior FV prediction performance of the RBFNN-GO model, and noise robustness testing showed it to be the most robust of the models evaluated.
- ❖
- SHAP analysis of the trained RBFNN-GO model revealed that both the FD and the MFV significantly influenced the FV predictions of DFs, with the MFV exerting somewhat greater influence than the FD.
- ❖
- PDP of the relative influence of the FD and the MFV on the RBFNN-GO model identified that the MFV and the FD exert complex and non-linear inter-relationships that influence the model’s FV predictions. Those relationships show that the model achieves its minimum FV predictions at the low FD values within the MFV range of 40 to 60 and its maximum FV predictions at the low MFV values, especially when the FD value is maximized.
- ❖
- Applying the RBFNN-GO model to ninety-nine unseen data points from another well in the studied fields demonstrated its high predictive accuracy, with an RMSE of 0.3227 mL and an R2 of 0.9624. These results confirm that the model is highly effective at accurately predicting the FV for other wells in the studied fields.
- ❖
- Reliable and regular estimation of the FV offers the potential to avoid formation damage by minimizing the mud filtration invasion and to reduce the risk of differential stuck drill-pipe incidents by minimizing the filter cake thickness.
- ❖
- The developed model improves upon the existing FV prediction models by making it possible to conduct semi-real-time monitoring of the FV during drilling operation with data inputs measured routinely at the well-site.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
Acronyms | |
ANNs | Artificial Neural Networks |
ARD | Automatic Relevance Determination |
COA | Cuckoo Optimization Algorithm |
DF | Drilling Fluids |
DL | Deep Learning |
ELM | Extreme Learning Machine |
GA | Genetic Algorithm |
GO | Growth Optimizer |
HML | Hybridized Machine Learning |
k-NN | k-Nearest Neighbor |
LSSVM | Least Square Support Vector Machine |
MELM | Multilayer Extreme Learning Machine |
MFV | Marsh Funnel Viscosity |
ML | Machine Learning |
OBDF | Oil-Based Drilling Fluids |
PDP | Partial Dependence Plot |
PSO | Particle Swarm Optimization |
RBFNN | Radial Basis Function Neural Network |
RF | Random Forest |
SBDF | Synthetic-Based Drilling Fluids |
SHAP | Shapley Additive Explanations |
SVM | Support Vector Machine |
WBDF | Water-Based Drilling Fluids |
XGB | XGBoost |
Parameters and variables | |
a20-index | An Index of Predictions within the Error Range of ±20% |
ARE | Average Relative Error |
FD | Fluid Density, pcf |
FV | Filtration Volume, mL |
HP/HT | High Pressure and High Temperature, °C |
GL | Gel Strength, lb./100 ft2 |
MFV | Marsh Funnel Viscosity, sec/quart |
MAPE | Mean Absolute Percentage Error |
OFI | Overfitting Index |
PV | Plastic Viscosity, cP |
PI | Performance Index |
R2 | Coefficient of Determination |
RMSE | Root-Mean-Square Error, mL |
RRMSE | Relative Root-Mean-Square Error |
S | Solid Percentage, % |
YP | Yield Point, lb./100 ft2 |
Appendix A. ML and Optimization Algorithms
Appendix A.1. Radial Basis Function Neural Network (RBFNN)
Appendix A.2. Multilayer Extreme Learning Machine (MELM)
Appendix A.3. Growth Optimizer (GO)
Appendix B. The Evaluation of Forecasting Prediction Performance
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Properties | Maximum | Minimum | Average | Skewness | Kurtosis |
---|---|---|---|---|---|
FD, pcf | 148 | 70 | 83.8477 | 2.6358 | 6.4458 |
MFV, sec/quart | 78 | 33 | 46.0324 | 1.7428 | 5.5073 |
FV, mL | 75.5 | 0.4 | 9.2294 | 3.5018 | 14.8659 |
GO Parameter | RBFNN | MELM |
---|---|---|
Maximum iteration | 100 | 100 |
Population | 30 | 50 |
P1 | 5 | 5 |
P2 | 0.001 | 0.001 |
P3 | 0.3 | 0.3 |
Type | Models | ARE | RMSE (mL) | RRMSE | R2 | PI | a20-Index |
---|---|---|---|---|---|---|---|
Simple | MELM | 0.0483 | 1.6583 | 0.2064 | 0.9213 | 0.1053 | 0.9497 |
RBFNN | 0.0081 | 0.8122 | 0.1011 | 0.9809 | 0.0508 | 0.9065 | |
Hybrid | MELM-GO | 0.0081 | 0.6687 | 0.0832 | 0.9870 | 0.0418 | 0.9638 |
RBFNN-GO | 0.0055 | 0.5919 | 0.0737 | 0.9898 | 0.0369 | 0.9682 |
Type | Models | ARE | RMSE (mL) | RRMSE | R2 | PI | a20-Index |
---|---|---|---|---|---|---|---|
Simple | MELM | −0.0083 | 2.4417 | 0.1221 | 0.9875 | 0.0612 | 1 |
RBFNN | 0.0024 | 0.9690 | 0.0485 | 0.9961 | 0.0242 | 1 | |
Hybrid | MELM-GO | −0.0059 | 0.9753 | 0.0488 | 0.9968 | 0.0244 | 0.9921 |
RBFNN-GO | −0.0072 | 0.6396 | 0.0320 | 0.9994 | 0.0160 | 1 |
Models | MELM | MELM-GO | RBFNN | RBFNN-GO |
---|---|---|---|---|
OFI | 0.0965 | 0.0383 | 0.0455 | 0.0327 |
Models | Subset | ARE | RMSE | RRMSE | R2 | PI | a20-Index | Score | Total Score |
---|---|---|---|---|---|---|---|---|---|
MELM | Train | 1 | 1 | 1 | 1 | 1 | 2 | 7 | 16 |
Test | 1 | 1 | 1 | 1 | 1 | 4 | 9 | ||
RBFNN | Train | 1 | 2 | 2 | 2 | 2 | 1 | 10 | 29 |
Test | 4 | 3 | 3 | 2 | 3 | 4 | 19 | ||
MELM-GO | Train | 1 | 3 | 3 | 3 | 3 | 3 | 16 | 31 |
Test | 3 | 2 | 2 | 3 | 2 | 3 | 15 | ||
RBFNN-GO | Train | 1 | 4 | 4 | 4 | 4 | 4 | 21 | 43 |
Test | 2 | 4 | 4 | 4 | 4 | 4 | 22 |
Properties | Maximum | Minimum | Average | Skewness | Kurtosis |
---|---|---|---|---|---|
FD, pcf | 87 | 74 | 79.9091 | 0.1960 | −1.5008 |
MFV, sec/quart | 48 | 30 | 40.5354 | −0.2419 | 1.0585 |
FV, mL | 9 | 2.5 | 5.01 | 0.1200 | −0.6400 |
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Davoodi, S.; Al-Rubaii, M.; Wood, D.A.; Al-Shargabi, M.; Mehrad, M.; Rukavishnikov, V.S. Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids. Appl. Sci. 2024, 14, 9035. https://doi.org/10.3390/app14199035
Davoodi S, Al-Rubaii M, Wood DA, Al-Shargabi M, Mehrad M, Rukavishnikov VS. Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids. Applied Sciences. 2024; 14(19):9035. https://doi.org/10.3390/app14199035
Chicago/Turabian StyleDavoodi, Shadfar, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, and Valeriy S. Rukavishnikov. 2024. "Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids" Applied Sciences 14, no. 19: 9035. https://doi.org/10.3390/app14199035
APA StyleDavoodi, S., Al-Rubaii, M., Wood, D. A., Al-Shargabi, M., Mehrad, M., & Rukavishnikov, V. S. (2024). Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids. Applied Sciences, 14(19), 9035. https://doi.org/10.3390/app14199035