No abstract

This paper's focus is the advocation of utilising diagnostic data available from digital field devices to help reduce operating costs for end users. In recent years companies across multiple industrial sectors have invested in improving their understanding of both the historical and live data they produce. The source of the data is specific to the processes but the objective for all remains the same - to use statistical techniques to develop a toolset that can be used to predict performance based on live and historical data. For the oil and gas industry, the continued adoption of digital device transmitters has increased the volume of data available from instruments such as flow meters, temperature probes and pressure sensors. Typically, this additional data provides information on the integrity or quality of the associated device. However, with the appropriate level of facility and instrument knowledge it is also possible to infer information with respect to the process stream. Furthermore, this data, if correctly interpreted, can be used to predict maintenance and calibration requirements, resulting in reduced staff effort and shutdowns. The need for physical intervention due to device failure is also reduced, which in turn minimises the potential for accidental hydrocarbon release when a device is removed for repair or replacement. NEL are currently undertaking research projects with the primary objective of developing definitive correlations between process effects, meter condition and diagnostic data response. The paper provides details of said research, with particular reference to the data science and mathematical techniques currently being trialed for the analysis stage. The techniques, when fully developed, will be metering technology specific and therefore offer a level of insight to end users on facility and meter performance which is not currently available in industry. The toolsets developed will in turn provide the end users with the knowledge and confidence to make cost saving decisions with respect to planned maintenance as well as improving facility efficiency through a more comprehensive understanding of their own data sets.

Heavy oils are characterised depending on their density rather than their viscosity [1]. Although there are various definitions for what constitutes heavy oil it is commonly agreed that the majority contain impurities such as asphaltenes, waxes and carbon residue. The API gravity definition, a common definition used in the oil and gas industry worldwide, states that heavy oil has an upper limit of 22°. Figure 1 shows the upper and lower limits of various categories of oil as stated in the API gravity definition. Confirmed world oil reserves are split approximately into 70% high viscosity and 30% (low viscosity) conventional light oils. High viscosity oils are regarded as a vital energy resource for the foreseeable future, with significant yields forecast at 100 years or more. A literature review conducted by NEL and Oxford University highlighted the issues facing application of conventional flow meters to high viscosity fluids [2]. Following on from the review, an initial experimental test programme [3] was instigated using a selection of conventional flow meters applied in viscous fluids. The overall conclusion from this work reinforced the notion that liquid flow meters cannot simply be relocated from low to high viscosity service without suitable characterisation or modification, nor can calibrations conducted in a low viscosity medium necessarily be applied to heavier crudes without appropriate compensation.