Deep Neural Network Prediction of Mechanical Drilling Speed
Abstract
:1. Introduction
2. Stratigraphic Profiles
3. Controlling Factors of ROP
3.1. Stratigraphy
3.2. Drilling Parameters
3.2.1. WOB
3.2.2. Rotating Speed
3.2.3. Pump Volume
3.2.4. Drilling Fluid Density
3.3. Drilling Tool
3.4. Analysis of Main Controlling Factors
4. A Prediction Model of ROP Based on DNN
4.1. Design of ROP Prediction Model
4.1.1. Input Layer, Middle Layer and Output Layer Selection
4.1.2. Weights, Bias and Activation Function Settings
4.1.3. Adam Optimizer
4.1.4. Dropout Mechanism
4.1.5. L2 Regularization
4.1.6. Calculation Process
- Data collection: collect acoustic time, drilling parameters, hydraulic parameters and ROP data.
- Data preprocessing: clean the collected data and fill the missing points with the average value.
- Data normalization: to coordinate the size of different parameters; for example, the numerical range of pump volume is 2000~3500, while the drilling fluid density range is mostly 1.3~1.4. The difference between the two is too large. In order to avoid the calculation error caused by the large gap between the data, as well as the problem of memory usage and slow calculation, the data is normalized.
- Data loading: input the processed data into the DNN.
- Nonlinear mapping: The DNN completes the nonlinear mapping calculation from the input layer to the output layer.
- Data inverse normalization: inverse normalization of output layer data.
- Data output: output the predicted ROP.
4.2. ROP Prediction Model Training
4.2.1. Training Process
4.2.2. Training Data Split
4.2.3. Training Strategy
4.3. Generalization Ability Test of ROP Prediction Model
5. ROP Predictive Model Applications
5.1. Geological Profile
5.2. Drilling Parameter Optimization Process
- Collection of design data of new well: use seismic method to invert the acoustic time data in the new well of the Liushagang Formation, and find the drilling parameter interval based on the design data of the new well and the history data of the completed adjacent wells.
- Prediction of ROP: take the design data as the input of the model and bring it into the DNN ROP prediction model for prediction.
- ROP judgment: the field operator judges whether the predicted ROP results meet the engineering expectations.
- Drilling parameter optimization: adjust the drilling parameters of the well section where the predicted ROP does not meet the engineering expectations, and adjust the main control factors according to the analysis results of the factors affecting the ROP in different layers of the Liushagang Formation.
- Re-prediction of ROP: the adjusted drilling parameters and unknown variables are taken as the input of the model and brought into the ROP prediction model based on DNN for prediction.
- Guide drilling: after the predicted ROP meets the actual engineering expectations, drill with the optimized drilling parameters.
6. Conclusions
- The ROP of the Liushagang Formation is mainly affected by stratigraphic lithology, and the controlling parameters on ROP are highly related to stratigraphic properties.
- The prediction model of ROP based on DNN shows good generalization ability and can meet the requirement of drilling engineering with a high enough accuracy.
- We also developed a framework or workflow for drilling parameter optimization. This workflow was validated by the simulation using data from the real field, and it can guide the optimization of drilling parameters to effectively improve the drilling speed.
- Compared with other data-driven ROP prediction models, the model developed in this work takes the formation conditions into account, and the prediction accuracy in complex formations can meet the requirements. However, the formation situation is more complex, and it is far from enough to replace the formation situation with the acoustic transit time only. Other parameters need to be added to the model to better describe the formation.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Stratum | Main Control Factors | Recommended Range |
---|---|---|
Section 1 of Liushagang Formation and upper part of Section 2 of Liushagang Formation | Rotating speed Drilling fluid density | [110, 120] r/min [1.33, 1.35] g/cm3 |
Lower part of Section 2 of Liushagang Formation | WOB | [60, 90] kN |
Section 3 of Liushagang Formation | WOB Rotating speed | [70, 90] kN [100, 120] r/min |
Name | Setting |
---|---|
Weights | normal distribution |
Bias | 0.1 |
Activation function | ReLU |
Test Well | Original Dataset | Training Set | Test Set |
---|---|---|---|
X-1 | 4155 | 3373 | 782 |
X-2 | 4155 | 2974 | 1181 |
X-3 | 4155 | 2908 | 1247 |
X-4 | 4155 | 3210 | 945 |
Hyperparameters | Setting |
---|---|
batch size | 100 |
epoch | 100 |
learning rate | 0.003 |
learning rate decay rate | 0.15 |
Well | Test Data | Average Relative Error |
---|---|---|
X-1 | 782 | 16.59% |
X-2 | 1181 | 14.06% |
X-3 | 1249 | 15.32% |
X-4 | 945 | 14.95% |
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Chen, H.; Jin, Y.; Zhang, W.; Zhang, J.; Ma, L.; Lu, Y. Deep Neural Network Prediction of Mechanical Drilling Speed. Energies 2022, 15, 3037. https://doi.org/10.3390/en15093037
Chen H, Jin Y, Zhang W, Zhang J, Ma L, Lu Y. Deep Neural Network Prediction of Mechanical Drilling Speed. Energies. 2022; 15(9):3037. https://doi.org/10.3390/en15093037
Chicago/Turabian StyleChen, Haodong, Yan Jin, Wandong Zhang, Junfeng Zhang, Lei Ma, and Yunhu Lu. 2022. "Deep Neural Network Prediction of Mechanical Drilling Speed" Energies 15, no. 9: 3037. https://doi.org/10.3390/en15093037