Prediction of dog-leg severity by using artificial neural network

Kaymak, Sinem
As technology growth, complexity of the drilling wells has been increasing. Directional wells have been drilling in order to deviate the well through planned targets which are at distant location from wellhead. One of the most important preliminary studies before drilling of any directional well is the prediction of Dog-leg severity. High and inconsistent Dog-leg severities can lead to high tortuosity, which may bring in high bottom torque, downhole tool failures, stuck pipe, target miss, inabilities to run casings, casing stuck and even side-track operations. Therefore, estimation of Dog-leg severity is vital for any directional wells. There are many variables affecting to DLS severity, which increases the complexity of estimation. Artificial Neural Networks (ANN) has become useful application for drilling industry since it is able to simulate highly non-linear relationships with large data sets. It is a statistical learning model inspired by biological neurons that connected and sending signals to each other. There are many Artificial Neural Network structures available. The most common one is “Feed-Forward Back Propagation Artificial Neural Network” known as; most accurate network due to generation of low error. This thesis is about estimation of Dog-leg severity of directional wells by Feed-Forward Back Propagation Artificial Neural Network. The study consists of two vi separate field drilling data, first one is an oil field located at Southeast of Turkey in Diyarbakir that has carbonates formation. Second one is a geothermal field located at the West of the Turkey in Manisa which has sandstone and claystone formation originated from metamorphic rocks. Two different ANN models have been created by considering 4290 individual drilling data of 12 wells for their 8 ½” hole sections in Diyarbakir Field and 1100 individual drilling data of 7 wells for their 12 ¼” hole sections in Manisa Field. Data sets have been prepared by dividing into 30m depth intervals. Parameters that affects Dog-leg severity are taken into account as input variables which are Sleeve Stabilizer Outer Diameter, String Stabilizer Outer Diameter, Downhole Motor Bent Angle, Rate of Penetration for bit wear effect, Depth, Inclination of the Wellbore, Tool Face Orientation, Weight on Bit, Bottom Revolution per Minute and Sliding Percentage. There are total 10 input variables drives 1 output variable which is Dog-leg Severity. Several sensitivity analyses have been made to decide network structure to obtain accurate, low error driven ANN model. It has been found that ANN Model is a proven tool for the estimation of DLS. Satisfactory results have been obtained with low Mean Squared Errors (MSE). MSE of Diyarbakir Field is 0.056 and, it is 0.057 for Manisa Field.


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Citation Formats
S. Kaymak, “Prediction of dog-leg severity by using artificial neural network,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Petroleum and Natural Gas Engineering., Middle East Technical University, 2019.