MSE Based Drilling Optimizer Project for Large National Drilling Contractor

2021 ◽  
Author(s):  
Imad Tawfiq Al Hamlawi ◽  
Andrew Creegan ◽  
Luis Ramon Baptista ◽  
Khaja Mohammed Azizuddin
Keyword(s):  
Artificial Intelligence ◽  
Cost Reduction ◽  
Control Mode ◽  
Drilling Rig ◽  
Advisory System ◽  
Mechanical Specific Energy ◽  
Drilling Parameters ◽  
Drilling Optimization ◽  
Reduction Efficiency ◽  
Conventional Drilling

Abstract A large GCC National Drilling Contractor is planning to trial a new MSE (Mechanical Specific Energy) Drilling advisory system based in artificial intelligence (AI) on a conventional drilling rig. This system works by means of calculating the optimal auto driller input parameters to achieve higher drilling efficiency. The objective of the MSE based Drilling Optimization system is to drive drilling efficiency by way of advising surface drilling parameters (WOB and RPM). The system will be tuned to advise/automatically modify WOB and RPM for each specific run, section, or well. The parameters can be adjusted in one of the two following ways: Advisory Mode: A recommended WOB and RPM value is sent to the driller, who then manually applies the setpoint change Control Mode: The setpoints are sent to the automatic driller for instant and automated application The system shall enable reduction of NPT and rig days to drill wells by increasing efficiency with consequent cost reduction efficiency by utilizing advanced elements of Artificial Intelligence (AI).

scholarly journals An Experimental Investigation of Drilling Performance Improvement Using Reaming While Drilling

2021 ◽  
pp. 1-17
Author(s):  
Abbas Roohi ◽  
Rahman Ashena ◽  
Gerhard Thonhauser ◽  
Thomas Finkbeiner ◽  
Laurent Gerbaud ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Specific Energy ◽  
Laboratory Experiments ◽  
Performance Indicator ◽  
Drill String ◽  
Drilling Rig ◽  
Mechanical Specific Energy ◽  
Drilling Performance ◽  
Drilling Bit ◽  
Conventional Drilling

Abstract This work investigates the drilling performance by reaming while drilling (RWD) using a dual-body bit and compared it with conventional drilling by a standard drilling bit. The dual-body bit consisted of a 2.45-in pilot bit located at a short distance ahead of a 2.47*3.97-in reamer. Conducting a series of drilling experiments at a simulation drilling rig with full monitoring sensors, we further studied the drilling performance as a function of the distance between the pilot bit and the reamer which affect mud diffusion and the resultant change in pore pressure and stress. A method was devised to eliminate the drill-string vibration and its effect on the drilling performance and the energy consumed. The mechanical specific energy (MSE) calculated for each case was considered as a drilling performance indicator. Using two laboratory experiments as well as analytical thermo-poro-elastic calculations of the Mechanical Specific Energy (MSE), the MSE changes were monitored and recorded. Comparison of this drilling performance indicator was used in both the RWD and the conventional drilling assembly to analyze the effect of RWD. Based on the results, with increasing the distance between the pilot bit and reamer, there is an increase in improvement of drilling performance in terms of MSE reduction. The best drilling performance indicator (MSE reduction of 84%) was observed with the distance between the pilot bit and the reamer of 43.3 cm. This is considered a novel finding in reaming while drilling.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

scholarly journals Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali ◽  
Mahmoud Abughaban ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Drilling Fluid ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Functional Networks ◽  
Lost Circulation ◽  
Drilling Parameters ◽  
High Pressure High Temperature ◽  
Drilling Cost

Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

Micro-Testing While Drilling for Rate of Penetration Optimization: Experiments and Simulations

2021 ◽  
pp. 1-49
Author(s):  
Magnus Nystad ◽  
Bernt Aadnoy ◽  
Alexey Pavlov
Keyword(s):  
Optimization Method ◽  
Extremum Seeking ◽  
Constraint Handling ◽  
Drilling Process ◽  
Test Results ◽  
Rate Of Penetration ◽  
Drilling Rig ◽  
Model Free ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking (ES) is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method which gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling within ES application to drilling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method's ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

Drilling Cutting Analysis to Assist Drilling Performance Evaluation in Hard Rock Hole Widening Operation

2020 ◽  
Author(s):  
Daiyan Ahmed ◽  
Yingjian Xiao ◽  
Jeronimo de Moura ◽  
Stephen D. Butt
Keyword(s):  
Performance Enhancement ◽  
Ore Deposits ◽  
Drilling Process ◽  
Rotary Drilling ◽  
Pilot Hole ◽  
Drilling Rig ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Weight On Bit ◽  
Drilling Operations

Abstract Optimum production from vein-type deposits requires the Narrow Vein Mining (NVM) process where excavation is accomplished by drilling larger diameter holes. To drill into the veins to successfully extract the ore deposits, a conventional rotary drilling rig is mounted on the ground. These operations are generally conducted by drilling a pilot hole in a narrow vein followed by a hole widening operation. Initially, a pilot hole is drilled for exploration purposes, to guide the larger diameter hole and to control the trajectory, and the next step in the excavation is progressed by hole widening operation. Drilling cutting properties, such as particle size distribution, volume, and shape may expose a significant drilling problem or may provide justification for performance enhancement decisions. In this study, a laboratory hole widening drilling process performance was evaluated by drilling cutting analysis. Drill-off Tests (DOT) were conducted in the Drilling Technology Laboratory (DTL) by dint of a Small Drilling Simulator (SDS) to generate the drilling parameters and to collect the cuttings. Different drilling operations were assessed based on Rate of Penetration (ROP), Weight on Bit (WOB), Rotation per Minute (RPM), Mechanical Specific Energy (MSE) and Drilling Efficiency (DE). A conducive schedule for achieving the objectives was developed, in addition to cuttings for further interpretation. A comprehensive study for the hole widening operation was conducted by involving intensive drilling cutting analysis, drilling parameters, and drilling performance leading to recommendations for full-scale drilling operations.

Application of Virtual Instrument in Geological Exploration Equipment

2013 ◽  
Vol 798-799 ◽  
pp. 302-306
Author(s):  
Yu Le Hu ◽  
Tao Yang ◽  
Guo Wei Yang
Keyword(s):  
Virtual Instrument ◽  
Drilling Process ◽  
Monitoring And Control ◽  
Geological Exploration ◽  
Monitoring And Control System ◽  
Engineering Software ◽  
Drilling Rig ◽  
Drilling Parameters ◽  
Instrument Technology ◽  
And Control

To ensure wire-line core drilling process more safe, efficient and quality, a real time monitoring and control system for drilling rig was built based on virtual instrument technology. Currently, the geological exploration have more precise requirements of many parameters, this system is very helpful for geological exploration. The system took IPC as main controller and used multi-sensors through DAQ card or serial port to monitor multi-parameters. Simultaneously, instructions ordered by host computer through digital-to-analog output card transmit to actuator, such as solenoid valve, to control drilling processs parameters. LabVIEW graphic oriented software platform provides a flexible and reliable support for drilling parameters supervisory control and data acquisition system. This applied engineering software offers a complete monitoring for several parameters of drilling rig.

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