Determining downhole operation transitions from wellbore measurement data using machine learning

A system, comprising:

• a processor;
• memory in electronic communication with the processor;
• and
• instructions stored in the memory, the instructions being executable by the processor to:
• perform a downhole sequence for a wellbore, the downhole sequence including a first downhole operation and a second downhole operation with a first transition between the first downhole operation and the second downhole operation;
• measure, by one or more downhole sensors or one or more surface sensors during the downhole sequence, time-series wellbore data for the downhole sequence of the wellbore;
• identify, in a downhole operation report for the wellbore, the first downhole operation, the second downhole operation, and a reported time signature for the first transition, wherein the reported time signature is inaccurate relative to an actual time signature when the first transition occurred between performance of the first downhole operation and performance of the second downhole operation in the downhole sequence for the wellbore;
• determine a plurality of time points within the time-series wellbore data and a plurality of corresponding time windows around the plurality of time points, wherein each time window of the plurality of corresponding time windows corresponds to a time point of the plurality of time points and includes, for the time point of the plurality of time points, a first portion before the time point of the plurality of time points and a second portion after the time point of the plurality of time points;
• for each time point of the plurality of time points, generate, from the time-series wellbore data, a statistical attribute set for a corresponding time window of the plurality of corresponding time windows, the statistical attribute set including a first statistical attribute subset for the first portion of the corresponding time window and a second statistical attribute set for the second portion of the corresponding time window;
• for each time point of the plurality of time points, predict a transition type at the time point using a transition identification machine learning model, the statistical attribute set for the corresponding time window being input to the transition identification machine learning model, the transition identification machine learning model having been trained to classify transitions between operations based on statistical attributes of wellbore data at time windows around transition times;
• identify, from the transition types for the plurality of time points, a predicted transition type corresponding to the first transition between the first downhole operation and the second downhole operation at a predicted time point with a predicted time signature;
• and
• in response to determining that the reported time signature for the first transition between the first downhole operation and the second downhole operation differs from the predicted time signature, update the reported time signature in the downhole operation report to the predicted time signature to reconcile the reported time signature for the first transition with the actual time signature at which the first transition occurred based on the time-series wellbore data for the downhole sequence.

A computer-implemented method for determining transitions in downhole operations, comprising:

• receiving reference time-series wellbore data measured for a set of reference wellbores;
• receiving reference downhole operation reports for the set of reference wellbores;
• identifying, from the reference downhole operation reports, reference time points corresponding to reference transitions between downhole operations in the set of reference wellbores;
• determining reference time windows around the reference time points in the reference time-series wellbore data;
• generating, from the reference time-series wellbore data, reference statistical attribute sets for the reference time windows;
• labeling the reference statistical attribute sets corresponding to the reference time points with corresponding ground-truth transition types from the reference downhole operation reports to create training data samples;
• training a transition identification machine learning model using the training data samples to predict transition types for the reference time points, including comparing predicted transition types for the reference time points to the corresponding ground-truth transition types for the reference time points;
• receiving time-series wellbore data measured for a downhole sequence in a wellbore by one or more downhole sensors or one or more surface sensors, the downhole sequence including a first downhole operation, a second downhole operation, and a first transition between the first downhole operation and the second downhole operation;
• identifying, in a downhole operation report for the wellbore, a reported time signature for the first transition between the first downhole operation and the second downhole operation, wherein the reported time signature is inaccurate relative to an actual time signature at which the first transition occurred between the first downhole operation and the second downhole operation in the downhole sequence for the wellbore;
• determining a plurality of time points within the time-series wellbore data and a plurality of corresponding time windows around the plurality of time points, wherein each time window of the plurality of time windows corresponds to a time point of the plurality of time points and includes, for a time point of the plurality of time points, a first portion before the time point of the plurality of time points and a second portion after the time point of the plurality of time points;
• for each time point of the plurality of time points, generating, from the time-series wellbore data, a statistical attribute set for a corresponding time window of the plurality of time windows, the statistical attribute set including a first statistical attribute subset for the first portion of the corresponding time window and a second statistical attribute subset for the second portion of the corresponding time window;
• for each time point of the plurality of time points, predicting a transition type at the time point using the transition identification machine learning model, the statistical attribute set for the corresponding time window being input to the transition identification machine learning model;
• identifying, from the transition types for the plurality of time points, a predicted transition type corresponding to the first transition between the first downhole operation and the second downhole operation at a predicted time point with a predicted time signature;
• and
• in response to determining that the reported time signature for the first transition differs from the predicted time signature, updating the reported time signature in the downhole operation report to the predicted time signature to reconcile the reported time signature with the actual time signature at which the first transition occurred, based on the time-series wellbore data for the wellbore.