In the development of an oil field, the assurance that produced hydrocarbon will flow is a critical determinant for the development of an oil and gas field even when other disciplines such as subsea design, reservoir engineering, artificial lift equipment, pipeline engineering have done a thorough analysis. Hydrate formation is one of the challenges of flow assurance in an oil and gas pipeline especially those installed in deepwater oil fields. Insulation of subsea pipelines is paramount to the prevention of hydrate formation. This work investigates the best material that is suitable for the thermal insulation of subsea flowlines using the ANSYS software package, and then provide the best composite arrangement of insulation materials for better heat optimisation. Four insulation materials were considered; Aerogel, Mineral Wool, Paraffin Wax and Grooved Mineral. A pipe-in-pipe (PiP) flowline model filled with fuel oil, at an inlet temperature of 60°C, was simulated on ANSYS by varying the configuration of the respective insulation material sheath; the heat transfer method in the PiP is by convection from the outer pipe to the ocean ambience at a temperature of 4°C. Results obtained from the simulation established that Aerogel is best for insulation among those considered, this is because of its low thermal conductivity and high specific heat capacity; in contrast, Grooved Mineral simulation result presented a poor performance. To ensure maximum heat conservation through optimum insulation, a composite configuration of insulation materials was proposed.
This project uses production data to generate well-specific correlations for GLR, BSW and sand concentration which are used for predictions. A software has been developed to effect a smart control algorithm. This results in a bean up or bean down operation depending on the current flowing conditions and constraints. Excel programming environment was used to write a code that constantly takes in measured data points, models the behavior of the individual data sets with bean size and controls the choke if the parameters of interest go above a predetermined cut-off. The software was also equipped with an inverse matrix solving algorithm that enables it to determine the choke performance constants for any set of initialization data. A set of data from field X were supplied and the choke performance constants; A, B, C, D and E, were found to be 10, 0.546, 0.0, 1.89 and 1.0 respectively. In addition to that, data from subsequent production operations were entered and the software was able to control the choke size to ensure that production stays below set constraints of 500, 80 and 10 in field units for GLR, BSW and sand concentration respectively. From this, it can be concluded that the software can effectively maintain the production of unwanted well effluents below their cut-offs, thereby improving oil production and the overall Net Profit Value (NPV) of a project.
This research entails evaluation of existing interfacial friction factor, gas-wall shear stress, and liquid wall shear stress correlations for the prediction of liquid holdup in pipelines. In addition, a statistical analysis was conducted on the predicted and measured flow parameters. Stratified horizontal two-phase flow equation was used in deriving an equation that solves for liquid holdup that is dependent on the interfacial shear stress. The model was implemented in a MATLAB integrated development environment to observe the effect of interfacial friction factors obtained from existing correlations. The results obtained from the comparative study of selected friction factors indicate that some of the correlations show high deviation from experimentally determined values. The largest deviation was observed in the model proposed by Sinai which was because of the condition for which it was originally developed is not suited for horizontal stratified two-phase gas-liquid flow. It was also observed that the correlation of Petalas and Aziz gave the best result and least deviation from the measured values. The performance of each correlation was observed to vary with the assumed values of liquid height. All the correlations gave good predictions at 30% liquid height but performed poorly at 40% liquid height.
NAPIMS is the investment portfolio management arm of the NNPC that has been entrusted with investments of the federal government of Nigeria in the oil and gas sector. In alignment with best international practices, NAPIMS adopted an efficient asset-based management organizational structure to monitor the true performance of the entire assets within its portfolio. Having an overview of all assets serves as a roadmap/link to obtaining vital background information about legacy performance as well as plans and strategies. Over the years, NAPIMS had a less structured asset overview for producing and non-producing assets thereby, encumbering information access for budgetary and budget performance tasks. The deployed methodology ensures that investments meet the long- and short-term financial objectives and risk tolerance of NNPC and Nigeria at large. This work bridged the performance gap between producing and non-producing assets by delineating important parameters for that influence asset portfolio management. This project also encompasses useful technical, commercial, planning, and financial parameters from sub-sections within an asset group. The developed and implemented solution offers good flexibility by providing every essential and minute detail that can help an asset manager to deep dive into the historical and current performance. It further warehouses the plans for future growth, expansion, and optimizations towards greater profitability.
This project uses production data to generate well-specific correlations for GLR, BSW and sand concentration which are used for predictions. A software has been developed to effect a smart control algorithm. This results in a bean up or bean down operation depending on the current flowing conditions and constraints. Excel programming environment was used to write a code that constantly takes in measured data points, models the behavior of the individual data sets with bean size and controls the choke if the parameters of interest go above a predetermined cut-off. The software was also equipped with an inverse matrix solving algorithm that enables it to determine the choke performance constants for any set of initialization data. A set of data from field X were supplied and the choke performance constants; A, B, C, D and E, were found to be 10, 0.546, 0.0, 1.89 and 1.0 respectively. In addition to that, data from subsequent production operations were entered and the software was able to control the choke size to ensure that production stays below set constraints of 500, 80 and 10 in field units for GLR, BSW and sand concentration respectively. From this, it can be concluded that the software can effectively maintain the production of unwanted well effluents below their cut-offs, thereby improving oil production and the overall Net Profit Value (NPV) of a project.
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