Data-driven performance and fault monitoring for oil production operations

The business objectives of a smart oilfield include: enhancing oil production, monitoring plant operations, improving product quality and ensuring worker and environmental safety. One of the most powerful levers for achieving these objectives is the field data. Decision making relies heavily on the field data. Therefore, data-driven techniques have gained great interest and have been beneficial for various areas of the petroleum industry. This dissertation proposes novel data-driven techniques to address three important issues for the oil production operations: 1. Control performance monitoring; 2. Quality-relevant fault detection; 3. Dynamic data reconstruction with missing and faulty records.;In remote operation of offshore platforms, real time control systems must be well maintained for efficient and safe operations. Early detection of control and equipment performance degradation is critical and is the foundation for implementing higher level integrated optimization. Poor control performance is usually the result of undetected deterioration in control valves, inadequate performance monitoring, and poor tuning in the controllers. In this dissertation, data-driven approaches to monitoring control performance are applied to an offshore platform. The minimum variance control benchmark for single loops and the covariance benchmark for multi-loops are used to detect deteriorated control variables. The covariance benchmark is used to determine the directions with significantly worse performance versus the benchmark. To detect valve stiction, the Savitzky-Golay smoothing filter is combined with a curve fitting method. The Savitzky-Golay filter has the advantage of preserving features of the distribution such as relative maxima, minima and widths. A stiction index is used to indicate whether a valve stiction occurs. The OSIsoft PI system is suggested as the implementation platform. Real-time data can be exchanged between PI and MATLAB via OPC interface.;To detect quality-relevant fault, a new concurrent projection to latent structures for the monitoring of output-relevant faults that affect the quality and input-relevant process faults is proposed. The input and output data spaces are concurrently projected to five subspaces, a joint input-output subspace that captures covariations between input and output, an output-principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Fault detection indices are developed based on the CPLS partition of subspaces for various fault detection alarms. The proposed CPLS monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces and could be incipient for the output. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed methods.;The field data are inevitably corrupted with errors and missing values. The quality of the oil field data significantly affects the oil production performance and the profit gained from using various software for process monitoring, online optimization, and process control. Missing or Faulty records will invalidate the information used for upper level production optimization. To improve the accuracy of the oil field data, new dynamic data reconstruction algorithms based on dynamic PCA are proposed. We propose both forward data reconstruction (FDR) and backward data reconstruction (BDR) approaches. Our approaches are very flexible that they can use partial data available at a particular time, and they are able to reconstruct missing or faulty records in situations that no matter how many sensors are missing or faulty. The effectiveness of our methods is illustrated with various missing data scenarios on an offshore production facility.