FIELD: petroleum industry.
SUBSTANCE: invention relates to the petroleum and gas field and can be used to predict sticking of a drilling pipe string in the process of drilling petroleum and gas boreholes using a neural network model. The method for predicting is implemented as follows. The parameters and modes of drilling are read from the geotechnical survey station, and the parameters from the borehole drilling process are measured and determined for the predicted drilled borehole; the values of such parameters as: X23 - the length of the non-cased part or the open section of the borehole and X24 - the length of a technological tool in the area of the open hole, are compared, if they (X23 and X24) are less than zero, the cycle is closed with the position until the values of X23 and X24 exceed zero. If the values of X23 and X24 are greater than zero, the values of the obtained parameters and modes are normalised and supplied to a prediction model, which, in turn, outputs a prediction for sticking: if the model predicts no sticking, the prediction procedure for the available input data at the time t+m is complete; if the model predicts sticking, the values of the parameters and modes of drilling need to be adjusted for the available input data at the time t+m, and the predicting procedure should be repeated for the input data with the prediction window offset at a time step t+m until a negative prediction for sticking is achieved.
EFFECT: increased accuracy of predicting and diagnosing the condition of a drilling pipe string for the risk of occurrence of sticking.
1 cl, 7 dwg
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Authors
Dates
2021-08-12—Published
2020-10-20—Filed