Intelligent Prediction of Sampling Time for Offshore Formation Testing Based on Hybrid-Driven Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Proxy Model Development
2.2. Constructing Training Data
2.3. Data Processing and Construction of a Large Digital Twin Database
2.4. Correlation Analysis
2.5. Construction of Hybrid-Driven Model
3. Results and Discussion
3.1. Correlation Enhancement
3.2. Optimal Model Selection
3.3. Model Parameter Optimization
3.4. Optimal Model Performance
3.5. Comprehensive Discussion and Practical Implications
4. Conclusions
- This study established a digital twin model for downhole formation testers to simulate the process of obtaining pure fluid samples, forming a large database of sampling simulations.
- In the prediction of pure fluid sampling time, this research model improved the data feature correlation through physical formulas and combined machine learning to establish a hybrid-driven model; the accuracy of the model improved by 74.21%. Moreover, on high-quality processed data, the optimal selected model outperformed others by 3.25% in accuracy and post-parameter optimization; it improved accuracy by 3.22% compared to before optimization. The final accuracy of the model is 95.12%.
- Based on simulated cleaning process data, this study devised an intelligent prediction method, enabling rapid forecasting of the onset time for pure formation fluid extraction without the need for modeling on offshore platforms. It has the advantages of accuracy, speed, and real-time feedback. Subsequently, it will play a crucial role in determining the timing of downhole fluid sampling.
- In future research, efforts can be directed toward expanding the data sources and dimensions to enhance the model’s generalizability. Additionally, optimizing algorithms through model integration and adaptive learning can improve performance robustness. Strengthening interpretability and stability also presents opportunities for further refinement.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Abbreviation | Full Term |
OBM | Oil-based mud |
SBM | Synthetic-based mud |
MLP | Multilayer perceptron |
AI | Artificial intelligence |
DFA | Downhole fluid analysis |
OCM | OBM-contamination-monitoring |
Pchip | Piecewise cubic hermite interpolating polynomial |
MSE | Mean squared error |
R2 | R-squared (coefficient of determination) |
Adam | Adaptive moment estimation |
Nadam | Nesterov-accelerated adaptive moment estimation |
RMSprop | Root mean square propagation |
Adagrad | Adaptive gradient algorithm |
Adadelta | Adaptive learning rate method |
FTRL | Follow-the-regularized-leader |
kv/kh | Permeability anisotropy |
rw | Wellbore diameter |
H | Formation thickness |
h | Relative tool distance from formation top |
vrat | Formation-fluid/mud-filtrate viscosity ratio |
wspc | Wellbore sampling contamination |
wvpr | Wellbore volume pump rate |
wvpt | Wellbore volume pump time |
wbhp | Wellbore bottom hole pressure |
SVM | Support vector machine |
XGBoost | Extreme gradient boosting |
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Author | Year | Input | Description | Output |
---|---|---|---|---|
O.C. Mullins [6] | 2000 | OFA data | Developed an optical fluid-recognition module for real-time monitoring using OFA data to determine the percentage of OBM filtrate contamination during sampling | Filtrate contamination |
M.E. Chenevert [12] | 2001 | Filtration measurement data of 100 water-based muds | Developed a theory to predict mud cake buildup and filtrate invasion, measured various filtration characteristics of water-based muds, and developed a corresponding numerical simulator | Prediction of mud cake buildup and filtrate invasion |
J. Wu [9] | 2004 | Formation parameters | Developed an effective time-dependent flow rate function that captures the effects of mud cake buildup to simulate complex mud filtrate invasion scenarios | Simulation of mud filtrate invasion |
K. Hsu [8] | 2006 | Parameters of OBM filtrate contamination | Studied the physical mechanisms of OBM filtrate contamination cleanup and constructed and validated a numerical model capable of handling multicomponent fluid flow and the thermodynamics of phase behavior. | Numerical model output |
Del Campo [7] | 2006 | Drilling mud filtrate and reservoir fluid samples | This paper introduced a new “focused sampling” device that rapidly separates drilling mud filtrate, improving sample quality, and presented real-time fluid characterization techniques to optimize the sampling process | Faster acquisition of clean fluid samples and real-time fluid property information |
Bon Johannes [13] | 2007 | Methods for collecting reservoir fluid samples and downhole conditions | Explores the impact of downhole conditions and fluid characteristics on sample quality as well as the application of single-phase and isokinetic sampling methods | Quality and accuracy of representative fluid samples |
A. Hadibeik [14] | 2009 | Formation parameters | Developed a 3D multiphase, multicomponent reservoir simulator considering the gravity and capillary pressure, studied the impact of the pollution function, and evaluated the impact of sampling time on fluid sample quality | Sampling time |
F.O. Alpak [15] | 2015 | Probe shape | The shape and layout of the sampling probe are crucial for obtaining low-contamination samples in a short time | Sampling time |
J.Y. Zuo [10] | 2015 | Sensor fluid characteristic measurement data | Developed a pollution monitoring workflow based on multiple sensor fluid characteristic measurements, improving the accuracy and robustness of pollution quantification | Contamination degree |
R. Lee [11] | 2016 | Fluid density and resistivity measurement data | Proposed a new water-sampling contamination quantification method applicable to all fluid combinations, demonstrating its effectiveness and robustness through multiple case studies | Contamination degree |
Parameter | Min Value | Max Value | Mean | Median | Standard Deviation | Skewness | Unit |
---|---|---|---|---|---|---|---|
Wellbore Diameter | 2.9375 | 6.125 | 4.53 | 4.53 | 0.93 | 0 | inch |
Radius of Filtrate Invasions | 2 | 30 | 16 | 16 | 8.17 | 0 | inch |
Permeability Anisotropy | 0.01 | 100 | 0 | 0 | 1.167 | 0 | - |
Formation Thickness | 0.5 | 100 | 50 | 50 | 30 | 0 | ft |
Fluid Viscosity Ratio | 0.01 | 100 | 0 | 0 | 1.167 | 0 | - |
Relative Tool Distance | 0 | 0.5 | 0.25 | 0.25 | 0.15 | 0 | - |
Before Processing | After Processing |
---|---|
t | |
h | h |
doi | |
rw | |
H | |
Model | R2 | MSE | Mean Absolute Percentage Error (%) |
---|---|---|---|
MLP | 0.9969 | 0.0107 | 8.0997 |
Support Vector Machine | 0.9872 | 0.0322 | 11.3467 |
Xgboost | 0.9691 | 0.1064 | 30.9935 |
Decision Tree | 0.7721 | 0.7857 | 258.1168 |
Linear Regression | 0.6622 | 1.1647 | 1820.6289 |
Optimizer | R2 | Loss |
---|---|---|
Adam | 0.9976 | 0.0080 |
Adamax | 0.9974 | 0.0085 |
RMSprop | 0.9894 | 0.0356 |
Adagrad | 0.9849 | 0.0507 |
Nadam | 0.9837 | 0.0548 |
Adadelta | 0.7881 | 0.7140 |
FTRL | 0.6639 | 1.1324 |
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Share and Cite
Nie, Y.; Li, C.; Zhou, Y.; Yu, Q.; Zuo, Y.; Meng, Y.; Xian, C. Intelligent Prediction of Sampling Time for Offshore Formation Testing Based on Hybrid-Driven Methods. J. Mar. Sci. Eng. 2024, 12, 1348. https://doi.org/10.3390/jmse12081348
Nie Y, Li C, Zhou Y, Yu Q, Zuo Y, Meng Y, Xian C. Intelligent Prediction of Sampling Time for Offshore Formation Testing Based on Hybrid-Driven Methods. Journal of Marine Science and Engineering. 2024; 12(8):1348. https://doi.org/10.3390/jmse12081348
Chicago/Turabian StyleNie, Yiying, Caoxiong Li, Yanmin Zhou, Qiang Yu, Youxiang Zuo, Yuexin Meng, and Chenggang Xian. 2024. "Intelligent Prediction of Sampling Time for Offshore Formation Testing Based on Hybrid-Driven Methods" Journal of Marine Science and Engineering 12, no. 8: 1348. https://doi.org/10.3390/jmse12081348