scholarly journals Real-time Surveillance System of Mechanical Specific Energy Applied in Drilling Parameters Optimization

2016 ◽  
Author(s):  
Yong-Xing Sun ◽  
Li-Hua Qiao ◽  
Hai-Fang Sun ◽  
Lie-Xiang Han ◽  
Guo Zhang
Keyword(s):  
Real Time ◽  
Specific Energy ◽  
Surveillance System ◽  
Parameters Optimization ◽  
Mechanical Specific Energy ◽  
Drilling Parameters

Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System

2021 ◽  
Author(s):  
Kingsley Williams Amadi ◽  
Ibiye Iyalla ◽  
Yang Liu ◽  
Mortadha Alsaba ◽  
Durdica Kuten
Keyword(s):  
Real Time ◽  
Specific Energy ◽  
Penetration Rate ◽  
Drilling Process ◽  
Stick Slip ◽  
Conservation Of Energy ◽  
Rate Of Penetration ◽  
Mechanical Specific Energy ◽  
Drilling Parameters ◽  
Drilling System

Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :

Drilling Parameters Optimization Using an Innovative Artificial Intelligence Model

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Rahman Ashena ◽  
Minou Rabiei ◽  
Vamegh Rasouli ◽  
Amir H. Mohammadi ◽  
Siamak Mishani
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Critical Factor ◽  
Drill String ◽  
Pattern Search ◽  
Parameters Optimization ◽  
Ann Model ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Drilling System

Abstract Proper selection of the drilling parameters and dynamic behavior is a critical factor in improving drilling performance and efficiency. Therefore, the development of an efficient artificial intelligence (AI) method to predict the appropriate control parameters is critical for drilling optimization. The AI approach presented in this paper uses the power of optimized artificial neural networks (ANNs) to model the behavior of the non-linear, multi-input/output drilling system. The optimization of the model was achieved by optimizing the controllers (combined genetic algorithm (GA) and pattern search (PS)) to reach the global optima, which also provides the drilling planning team with a quantified recommendation on the appropriate optimal drilling parameters. The optimized ANN model used drilling parameters data recorded real-time from drilling practices in different lithological units. Representative portions of the data sets were utilized in training, testing, and validation of the model. The results of the analysis have demonstrated the AI method to be a promising approach for simulation and prediction of the behavior of the complex multi-parameter drilling system. This method is a powerful alternative to traditional analytic or real-time manipulation of the drilling parameters for mitigation of drill string vibrations and invisible lost time (ILT). The utilization can be extended to the field of drilling control and optimization, which can lead to a great contribution of 73% in reduction of the drilling time.

scholarly journals Optimization of Drilling Parameters by Analysis of Formation Strength Properties with Utilization of Mechanical Specific Energy

2017 ◽  
Vol 07 (11) ◽  
pp. 1590-1602 ◽  
Author(s):  
Afshin Davarpanah ◽  
Seyed Mohammad Mehdi Nassabeh ◽  
Behnam Mirshekari
Keyword(s):  
Specific Energy ◽  
Strength Properties ◽  
Mechanical Specific Energy ◽  
Drilling Parameters

scholarly journals Real-Time Minimization of Mechanical Specific Energy with Multivariable Extremum Seeking

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1298
Author(s):  
Magnus Nystad ◽  
Bernt Sigve Aadnøy ◽  
Alexey Pavlov
Keyword(s):  
Real Time ◽  
Specific Energy ◽  
Optimization Method ◽  
Initial Guess ◽  
Drill String ◽  
Extremum Seeking ◽  
Drilling Process ◽  
Field Development ◽  
Mechanical Specific Energy ◽  
Time Minimization

Drilling more efficiently and with less non-productive time (NPT) is one of the key enablers to reduce field development costs. In this work, we investigate the application of a data-driven optimization method called extremum seeking (ES) to achieve more efficient and safe drilling through automatic real-time minimization of the mechanical specific energy (MSE). The ES algorithm gathers information about the current downhole conditions by performing small tests with the applied weight on bit (WOB) and drill string rotational rate (RPM) while drilling and automatically implements optimization actions based on the test results. The ES method does not require an a priori model of the drilling process and can thus be applied even in instances when sufficiently accurate drilling models are not available. The proposed algorithm can handle various drilling constraints related to drilling dysfunctions and hardware limitations. The algorithm’s performance is demonstrated by simulations, where the algorithm successfully finds and maintains the optimal WOB and RPM while adhering to drilling constraints in various settings. The simulations show that the ES method is able to track changes in the optimal WOB and RPM corresponding to changes in the drilled formation. As demonstrated in the simulation scenarios, the overall improvements in rate of penetration (ROP) can be up to 20–170%, depending on the initial guess of the optimal WOB and RPM obtained from e.g., a drill-off test or a potentially inaccurate model. The presented algorithm is supplied with specific design choices and tuning considerations that facilitate its simple and efficient use in drilling applications.

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