Method for determining downhole characterics in a production well

Abstract

A method for determining one or more downhole characteristic(s) in a production well, comprising: measuring a physical property of the production well using at least one sensor; pre-processing the measured values of the physical property using feature scaling or automatic gain control techniques; and determining one or more downhole characteristic(s) using a supervised machine learning method. The supervised machine learning method comprises: cross-validating a predictive analytics algorithm with training data that are previously measured physical properties and applying one or more algorithms to the measured values of the physical property to converge on an estimate for the determined downhole characteristic(s).

Description

FIELD OF THE INVENTION[0001]The present invention relates to a method for determining downhole characteristics in a production well.BACKGROUND ART[0002]Production wells are an important part of certain industries, especially for mining and oil drilling. When performing various types of operations downhole, such as drilling, cutting, etc., it is desirable to monitor the downhole characteristics during the operation. However, in practice, it is often difficult to achieve accurate measurements of downhole characteristics. For example, downhole measurements usually require interruption of production, which costs precious operational time. Yet further, while certain measurements may be made, not all downhole characteristics are easily measured.[0003]Traditional approaches that determine downhole characteristics in a production well require certain physical models or assumptions. In practice, it is very challenging to get accurate and reliable models. In most cases, the designed models ar...

AI Technical Summary

Benefits of technology

The patent text discusses a method to improve the accuracy of machine learning classifiers by using feature scaling and an automatic gain control technique to change the distribution of data across possible values. This pre-processing helps to overcome data imbalance and make the machine learning classifier more accurate in its performance.

Problems solved by technology

However, in practice, it is often difficult to achieve accurate measurements of downhole characteristics.

For example, downhole measurements usually require interruption of production, which costs precious operational time.

Yet further, while certain measurements may be made, not all downhole characteristics are easily measured.

In practice, it is very challenging to get accurate and reliable models.

In most cases, the designed models are simplifying the downhole environment too much.

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