Industry Threshold
Machine Learning for Improved Directional Drilling Hani Elshahawi
Digitalization Lead - Deepwater Technologies at Shell International Exploration and Production Inc. Abstract Directional drilling is a complex process involving the remote control of tool alignment and force application to a very long drill string subject to variable external forces. Controlling bit tool face orientation while ensuring adequate rate of penetration (ROP) is quite challenging.
of expert directional drillers and drilling simulations (Figure 1) and generate predictions or decision-making based on complex patterns in previously collected data.
operation as well as the resulting effects on the drilling tool and wellbore. Much of this information is recorded in the drilling logs and includes differential pressure, rotary torque, hook
The project was broken down into the following tasks to achieve the stated objectives: information formulation including Operator engagement and data preparation, immersion and analysis including artificial neural network construction, evaluation, drilling simulation, and reinforcement learning.
load, tool face angle, and ROP as well as planned and estimated actual wellbore trajectory. The drilling operator in turn controls weight on bit (WOB), flow rate (GPM), rotary speed (RPM), and top drive center position and torque. Knowledge-sharing meetings between directional drilling domain experts and artificial intelligence data scientists enabled determination of viable learning goals and informed the design of the artificial neural network. Raw data from directional drilling logs was processed, filtered, and parsed to feed it into the custom machine learning system.
An artificial intelligence system was developed to learn from the actions of expert directional drillers and the mechanics of drilling simulations. Machine learning algorithms were employed to improve the efficiency of directional drilling: optimized ROP, less tortuous borehole, less personnel on board (POB), and consistency across operations. To create a system for controlling tool face angle and guiding drill bit sliding during directional drilling, relevant historical data from directional drilling operations was gathered. Much of this data was recorded in the drilling logs, which the drilling operator traditionally uses to control drilling parameters. The collected data was then filtered and used to structure and train artificial neural networks and select appropriate hyperparameters. The neural network developed could replicate the decisions of expert directional drillers within a small error (<3%). Reinforcement learning was then successfully used to improve network performance, particularly for conditions not previously considered. Methods, Procedures, and Process We have engaged in the development of a deep learning, artificial intelligence system to learn from the actions
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Before machine learning can commence, it is essential to gather and understand the relevant data. Data scientists engaged with Operator subject matter experts to transfer data that was used to structure and train the neural network. In the case of a directional drilling system, the network requires any available information a driller used when making decisions about slides in a drilling
The gathered datasets were divided into three separate sets to train and validate the results; training, validation, and testing. Training data is used
ECHO - Feb 2019
