Drilling leakage probability prediction method based on naive Bayesian algorithm

Abstract

The invention belongs to the field of drilling leakage prediction and the field of artificial intelligence machine learning, and particularly relates to a drilling leakage probability prediction method based on a naive Bayesian algorithm. The method comprises the following steps: extracting oil reservoir historical development data from an oil reservoir well history database of a certain oil reservoir development area, preprocessing the oil reservoir historical development data, creating a sample set and a test set, determining characteristic attributes and an output category space from the sample data, and counting the prior probability of each characteristic attribute value according to drilling leakage parameter data of the sample set; calculating conditional probabilities by adopting priori probability learning, calculating by adopting different conditional probabilities when feature attribute values are continuous values and discrete values, calculating corresponding conditional probabilities by selecting a mode of combining every two features to improve the generalization performance of the model, jointly calculating posteriori probabilities by adopting the priori probabilities, and establishing a naive Bayesian model classifier; performing verification by adopting the verification set, and inputting the real-time drilling parameter data into the model to obtain the corresponding leakage probability.

Description

technical field [0001] The invention belongs to the fields of drilling loss prediction and artificial intelligence machine learning, and in particular relates to a drilling loss probability prediction method based on a naive Bayesian algorithm. Background technique [0002] Lost circulation is a complex and common working condition in the drilling process, which will cause serious harm and economic loss to the drilling industry. During the drilling process, many drilling parameters will affect lost circulation, such as well depth, layer, lithology, weight on bit, torque, speed of penetration, vertical pressure, inlet flow, outlet flow, porosity, permeability, drilling fluid density, drilling Liquid-solid phase content, Young's modulus, bit type, bit size, hook load, etc. The changes of these drilling parameters during the drilling process may make the drilling fluid column pressure greater than the formation rock fracture pressure, thereby forming induced fractures, or reop...

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Problems solved by technology

[0005] In order to overcome the insufficiency and limitation of the existing lost circulation prediction methods, which cause the well site technicians to be unable to control the corresponding lost circulation parameters, making the lost circulation difficult to control and occur repeatedly, and enrich the methods of predicting lost circulation by using drilling parameters , the present invention proposes a drilling loss probability prediction method based on naive Bayesian algorithm

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