Slug monitoring and forecasting in production flowlines through artificial intelligence
AssigneeSchlumberger Technology Corporation
InventorsDinesh Sonu Dabholkar, Sunil Manikani, Devina Rawat
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Abstract
A method for multi-horizon forecasting of gas-liquid slug flow is provided. Field data for a well is obtained. The field data comprises a plurality of features. The plurality of features is correlated across a set of historic data to generate time series data for each of the plurality of features. The time series data is processed by a machine learning model to generate a multi-horizon forecast of a flow pattern for the well, and the output is presented.
Claims20
A method for multi-horizon forecasting of gas-liquid slug flow, comprising:
- obtaining field data for a well, the field data comprising a plurality of features;
- correlating the plurality of features across a set of historic data to generate time series data for each of the plurality of features;
- processing the time series data by a machine learning model to generate a multi-horizon forecast of a flow pattern for the well;
- and
- presenting the output.
A computer program product comprising non-transitory computer-readable program code that, when executed by a computer processor of a computing system, cause the computing system to perform the method of:
- obtaining field data for a well, the field data comprising a plurality of features;
- correlating the plurality of features across a set of historic data to generate time series data for each of the plurality of features;
- processing the time series data by a machine learning model to generate a multi-horizon forecast of a flow pattern for the well;
- and
- presenting the output.
A system comprising:
- a computer processor;
- memory;
- and
- instructions stored in the memory and executable by the computer processor to cause the computer processor to perform operations, the operations comprising:
- obtaining field data for a well, the field data comprising a plurality of features;
- correlating the plurality of features across a set of historic data to generate time series data for each of the plurality of features;
- processing the time series data by a machine learning model to generate a multi-horizon forecast of a flow pattern for the well;
- and
- presenting the output.
Dependencies are inferred from the claim text.