| CPC G01V 5/12 (2013.01) [E21B 47/085 (2020.05); G01V 1/50 (2013.01); G01V 5/0025 (2013.01); G06N 20/00 (2019.01); E21B 2200/20 (2020.05)] | 19 Claims |
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1. A method for well integrity assessment across a depth interval of a cased wellbore having one or more casing strings using machine learning, comprising:
a. collecting scattered X-ray signals from a plurality of X-ray detectors arranged within a well logging tool while that tool operates in one or more wellbores with known wellbore characteristics;
b. associating the known wellbore characteristics with the collected scattered X-ray signals;
c. training a machine learning model using the collected scattered X-ray signals and associated wellbore characteristics to produce a trained model that predicts wellbore characteristics from scattered X-ray signals;
d. collecting further scattered X-ray signals from the plurality of X-ray detectors arranged within a well logging tool while that tool operates in a wellbore with unknown wellbore characteristics;
e. applying the trained machine learning model to the collected further scattered X-ray signals to predict unknown wellbore characteristics; and
f. assessing the well integrity using the predicted wellbore, characteristics,
g. wherein the steps of collecting the scattered X-ray signals and collecting the further scattered X-ray signals each comprise collecting the signals synchronously from all of the plurality of detectors, and
h. wherein the steps of training the machine learning model using the scattered X-ray signals arising from X-rays above one or more energy thresholds and applying the trained machine learning model to the further scattered X-ray signals arising from X-rays above one or more energy thresholds comprise training the machine learning model and applying the trained machine learning model using X-ray signals from two or more energy thresholds in conjunction.
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