Schlumberger Technology Corporation

Services Petroliers Schlumberger

Maja Skataric

Sandip Bose

Smaine Zeroug

Bikash Kumar Sinha

Shuchin Aeron

Bo Fan

Ram Sunder Kalyanaraman

Erik Wielemaker

A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

Methods of analyzing cement integrity in annuli of a multiple-cased well using machine learning

Methods arc provided for using sonic tool data to investigate a multi-string wcllbore. The sonic data is processed to obtain indications of phase slowness dispersions for multiple locations in the wellbore. The dispersions are aggregated. The aggregated dispersions are compared with a plurality of cut-off mode templates to identify the presence of cut-off modes or the lack thereof in the aggregated phase slowness dispersions. Features of the multi-string wellbore are identified based on the presence of the cut-off modes or the lack thereof. In another method, the sonic data is processed to obtain indications as a function of depth of at least one of an energy spectrum, a semblance projection, a slowness dispersion projection, an attenuation dispersion projection, and a wavenumber dispersion projection. The indications are inspected to locate a shift at a particular depth indicat- ing a transition in at least oneannulus of the multi-string wellbore.

Well integrity analysis using sonic measurements over depth interval

The present disclosure provides methods and systems for analyzing cement integrity in a depth interval of a wellbore having a multiple string casing with an innermost annulus disposed inside at least one outer annulus. The method includes processing ultrasonic data obtained from ultrasonic measurements on the interval of the wellbore to determine properties of the innermost annulus. The method also includes processing sonic data obtained from sonic measurements on the interval of the wellbore to extract features of the sonic data. The features of the sonic data are input to a machine learning processing to determine properties of both the innermost annulus and the least one outer annulus. Additional processing of ultrasonic and sonic data can also be used to determine properties of both the innermost annulus and the least one outer annulus. These properties can be used to analyze cement integrity in the depth interval of the wellbore.

Method for analyzing cement integrity in casing strings using machine learning

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Cambridge, MA, United States of America

Maja Skataric