Application of Neural Networks for Predictive Control in Drilling Dynamics
- D. Dashevskiy (University of Houston) | V. Dubinsky (Baker Hughes Inteq) | J.D. Macpherson (Baker Hughes Inteq)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 3-6 October, Houston, Texas
- Publication Date
- Document Type
- Conference Paper
- 1999. Society of Petroleum Engineers
- 1.6.3 Drilling Optimisation, 1.5 Drill Bits, 6.1.5 Human Resources, Competence and Training, 1.12.1 Measurement While Drilling, 1.6 Drilling Operations, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 4.3.4 Scale, 1.2.2 Drilling Optimisation, 1.11.2 Drilling Fluid Selection and Formulation (Chemistry, Properties), 1.10 Drilling Equipment, 7.6.6 Artificial Intelligence, 1.2.5 Drilling vibration management, 1.6.1 Drilling Operation Management, 5.5.1 Simulator Development
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Real-time monitoring of BHA and drill bit dynamic behavior is a critical factor in improving drilling efficiency. It allows the driller to avoid detrimental drillstring vibrations and maintain optimum drilling conditions through periodic adjustments to various surface control parameters (such as hook load, RPM, flow rate and mud properties). However, selection of the correct control parameters is not a trivial task. A few iterations in parameter modification may be required before the desired effect is achieved and, even then, the result may not be optimal. For this reason, the development of efficient methods to predict the dynamic behavior of the BHA, and methods to select the appropriate control parameters, is important for drilling optimization.
The approach presented in this paper uses the power of Neural Networks (NN) to model the dynamic behavior of the non-linear, multi- input/output drilling system. Such a model, along with an optimizing controller, provides the driller with a quantified recommendation on the appropriate corrective action(s) required to bring the system to an optimal drilling condition.
Development of the NN model used drilling dynamics data from a field test. This field test involved various drilling scenarios in different lithologic units. The training and fine-tuning of the basic model utilized both surface and downhole dynamics data recorded in real-time while drilling. Measurement of the dynamic state of the BHA was achieved using data from downhole vibration sensors. This information, which represents the effects of modifying the surface control parameters, was recorded in the memory of the downhole tool. Representative portions of this test data set, along with the corresponding set of input-output control parameters, were used in developing and training the model.
Test results are promising: There is good agreement between the dynamic behavior of the BHA predicted by the NN model and the actual measured BHA response. In addition, the test established criteria for selecting the most important input-output parameters and for selecting representative data sets for building and training the model.
This analysis has demonstrated a promising approach to simulation and prediction of the dynamic behavior of the complex multi-parameter drilling system. This method could become a powerful alternative to traditional analytic or direct numerical modeling and its utilization could be extended beyond drilling dynamics to the field of drilling control and optimization.
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